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// 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 "chrome/browser/thumbnails/content_analysis.h"
#include <algorithm>
#include <cmath>
#include <cstdlib>
#include <functional>
#include <limits>
#include <numeric>
#include <vector>
#include "base/memory/scoped_ptr.h"
#include "testing/gtest/include/gtest/gtest.h"
#include "third_party/skia/include/core/SkBitmap.h"
#include "third_party/skia/include/core/SkColor.h"
#include "ui/gfx/canvas.h"
#include "ui/gfx/color_analysis.h"
#include "ui/gfx/color_utils.h"
#include "ui/gfx/geometry/rect.h"
#include "ui/gfx/geometry/size.h"
#include "ui/gfx/image/image.h"
namespace {
#ifndef M_PI
#define M_PI 3.14159265358979323846
#endif
unsigned long ImagePixelSum(const SkBitmap& bitmap, const gfx::Rect& rect) {
// Get the sum of pixel values in the rectangle. Applicable only to
// monochrome bitmaps.
DCHECK_EQ(kAlpha_8_SkColorType, bitmap.colorType());
unsigned long total = 0;
for (int r = rect.y(); r < rect.bottom(); ++r) {
const uint8* row_data = static_cast<const uint8*>(
bitmap.getPixels()) + r * bitmap.rowBytes();
for (int c = rect.x(); c < rect.right(); ++c)
total += row_data[c];
}
return total;
}
bool CompareImageFragments(const SkBitmap& bitmap_left,
const SkBitmap& bitmap_right,
const gfx::Size& comparison_area,
const gfx::Point& origin_left,
const gfx::Point& origin_right) {
SkAutoLockPixels left_lock(bitmap_left);
SkAutoLockPixels right_lock(bitmap_right);
for (int r = 0; r < comparison_area.height(); ++r) {
for (int c = 0; c < comparison_area.width(); ++c) {
SkColor color_left = bitmap_left.getColor(origin_left.x() + c,
origin_left.y() + r);
SkColor color_right = bitmap_right.getColor(origin_right.x() + c,
origin_right.y() + r);
if (color_left != color_right)
return false;
}
}
return true;
}
float AspectDifference(const gfx::Size& reference, const gfx::Size& candidate) {
return std::abs(static_cast<float>(candidate.width()) / candidate.height() -
static_cast<float>(reference.width()) / reference.height());
}
} // namespace
namespace thumbnailing_utils {
class ThumbnailContentAnalysisTest : public testing::Test {
};
TEST_F(ThumbnailContentAnalysisTest, ApplyGradientMagnitudeOnImpulse) {
gfx::Canvas canvas(gfx::Size(800, 600), 1.0f, true);
// The image consists of a point spike on uniform (non-zero) background.
canvas.FillRect(gfx::Rect(0, 0, 800, 600), SkColorSetRGB(10, 10, 10));
canvas.FillRect(gfx::Rect(400, 300, 1, 1), SkColorSetRGB(255, 255, 255));
SkBitmap source = skia::ReadPixels(canvas.sk_canvas());
SkBitmap reduced_color;
reduced_color.allocPixels(SkImageInfo::MakeA8(source.width(),
source.height()));
gfx::Vector3dF transform(0.299f, 0.587f, 0.114f);
EXPECT_TRUE(color_utils::ApplyColorReduction(
source, transform, true, &reduced_color));
float sigma = 2.5f;
ApplyGaussianGradientMagnitudeFilter(&reduced_color, sigma);
// Expect everything to be within 8 * sigma.
int tail_length = static_cast<int>(8.0f * sigma + 0.5f);
gfx::Rect echo_rect(399 - tail_length, 299 - tail_length,
2 * tail_length + 1, 2 * tail_length + 1);
unsigned long data_sum = ImagePixelSum(reduced_color, echo_rect);
unsigned long all_sum = ImagePixelSum(reduced_color, gfx::Rect(800, 600));
EXPECT_GT(data_sum, 0U);
EXPECT_EQ(data_sum, all_sum);
sigma = 5.0f;
ApplyGaussianGradientMagnitudeFilter(&reduced_color, sigma);
// Expect everything to be within 8 * sigma.
tail_length = static_cast<int>(8.0f * sigma + 0.5f);
echo_rect = gfx::Rect(399 - tail_length, 299 - tail_length,
2 * tail_length + 1, 2 * tail_length + 1);
data_sum = ImagePixelSum(reduced_color, echo_rect);
all_sum = ImagePixelSum(reduced_color, gfx::Rect(800, 600));
EXPECT_GT(data_sum, 0U);
EXPECT_EQ(data_sum, all_sum);
}
TEST_F(ThumbnailContentAnalysisTest, ApplyGradientMagnitudeOnFrame) {
gfx::Canvas canvas(gfx::Size(800, 600), 1.0f, true);
// The image consists of a single white block in the centre.
gfx::Rect draw_rect(300, 200, 200, 200);
canvas.FillRect(gfx::Rect(0, 0, 800, 600), SkColorSetRGB(0, 0, 0));
canvas.DrawRect(draw_rect, SkColorSetRGB(255, 255, 255));
SkBitmap source = skia::ReadPixels(canvas.sk_canvas());
SkBitmap reduced_color;
reduced_color.allocPixels(SkImageInfo::MakeA8(source.width(),
source.height()));
gfx::Vector3dF transform(0.299f, 0.587f, 0.114f);
EXPECT_TRUE(color_utils::ApplyColorReduction(
source, transform, true, &reduced_color));
float sigma = 2.5f;
ApplyGaussianGradientMagnitudeFilter(&reduced_color, sigma);
int tail_length = static_cast<int>(8.0f * sigma + 0.5f);
gfx::Rect outer_rect(draw_rect.x() - tail_length,
draw_rect.y() - tail_length,
draw_rect.width() + 2 * tail_length,
draw_rect.height() + 2 * tail_length);
gfx::Rect inner_rect(draw_rect.x() + tail_length,
draw_rect.y() + tail_length,
draw_rect.width() - 2 * tail_length,
draw_rect.height() - 2 * tail_length);
unsigned long data_sum = ImagePixelSum(reduced_color, outer_rect);
unsigned long all_sum = ImagePixelSum(reduced_color, gfx::Rect(800, 600));
EXPECT_GT(data_sum, 0U);
EXPECT_EQ(data_sum, all_sum);
EXPECT_EQ(ImagePixelSum(reduced_color, inner_rect), 0U);
}
TEST_F(ThumbnailContentAnalysisTest, ExtractImageProfileInformation) {
gfx::Canvas canvas(gfx::Size(800, 600), 1.0f, true);
// The image consists of a white frame drawn in the centre.
gfx::Rect draw_rect(100, 100, 200, 100);
gfx::Rect image_rect(0, 0, 800, 600);
canvas.FillRect(image_rect, SkColorSetRGB(0, 0, 0));
canvas.DrawRect(draw_rect, SkColorSetRGB(255, 255, 255));
SkBitmap source = skia::ReadPixels(canvas.sk_canvas());
SkBitmap reduced_color;
reduced_color.allocPixels(SkImageInfo::MakeA8(source.width(),
source.height()));
gfx::Vector3dF transform(1, 0, 0);
EXPECT_TRUE(color_utils::ApplyColorReduction(
source, transform, true, &reduced_color));
std::vector<float> column_profile;
std::vector<float> row_profile;
ExtractImageProfileInformation(reduced_color,
image_rect,
gfx::Size(),
false,
&row_profile,
&column_profile);
EXPECT_EQ(0, std::accumulate(column_profile.begin(),
column_profile.begin() + draw_rect.x() - 1,
0));
EXPECT_EQ(column_profile[draw_rect.x()], 255U * (draw_rect.height() + 1));
EXPECT_EQ(2 * 255 * (draw_rect.width() - 2),
std::accumulate(column_profile.begin() + draw_rect.x() + 1,
column_profile.begin() + draw_rect.right() - 1,
0));
EXPECT_EQ(0, std::accumulate(row_profile.begin(),
row_profile.begin() + draw_rect.y() - 1,
0));
EXPECT_EQ(row_profile[draw_rect.y()], 255U * (draw_rect.width() + 1));
EXPECT_EQ(2 * 255 * (draw_rect.height() - 2),
std::accumulate(row_profile.begin() + draw_rect.y() + 1,
row_profile.begin() + draw_rect.bottom() - 1,
0));
gfx::Rect test_rect(150, 80, 400, 100);
ExtractImageProfileInformation(reduced_color,
test_rect,
gfx::Size(),
false,
&row_profile,
&column_profile);
// Some overlap with the drawn rectagle. If you work it out on a piece of
// paper, sums should be as follows.
EXPECT_EQ(255 * (test_rect.bottom() - draw_rect.y()) +
255 * (draw_rect.right() - test_rect.x()),
std::accumulate(row_profile.begin(), row_profile.end(), 0));
EXPECT_EQ(255 * (test_rect.bottom() - draw_rect.y()) +
255 * (draw_rect.right() - test_rect.x()),
std::accumulate(column_profile.begin(), column_profile.end(), 0));
}
TEST_F(ThumbnailContentAnalysisTest,
ExtractImageProfileInformationWithClosing) {
gfx::Canvas canvas(gfx::Size(800, 600), 1.0f, true);
// The image consists of a two white frames drawn side by side, with a
// single-pixel vertical gap in between.
gfx::Rect image_rect(0, 0, 800, 600);
canvas.FillRect(image_rect, SkColorSetRGB(0, 0, 0));
canvas.DrawRect(gfx::Rect(300, 250, 99, 100), SkColorSetRGB(255, 255, 255));
canvas.DrawRect(gfx::Rect(401, 250, 99, 100), SkColorSetRGB(255, 255, 255));
SkBitmap source = skia::ReadPixels(canvas.sk_canvas());
SkBitmap reduced_color;
reduced_color.allocPixels(SkImageInfo::MakeA8(source.width(),
source.height()));
gfx::Vector3dF transform(1, 0, 0);
EXPECT_TRUE(color_utils::ApplyColorReduction(
source, transform, true, &reduced_color));
std::vector<float> column_profile;
std::vector<float> row_profile;
ExtractImageProfileInformation(reduced_color,
image_rect,
gfx::Size(),
true,
&row_profile,
&column_profile);
// Column profiles should have two spikes in the middle, with a single
// 0-valued value between them.
EXPECT_GT(column_profile[398], 0.0f);
EXPECT_GT(column_profile[399], column_profile[398]);
EXPECT_GT(column_profile[402], 0.0f);
EXPECT_GT(column_profile[401], column_profile[402]);
EXPECT_EQ(column_profile[401], column_profile[399]);
EXPECT_EQ(column_profile[402], column_profile[398]);
EXPECT_EQ(column_profile[400], 0.0f);
EXPECT_EQ(column_profile[299], 0.0f);
EXPECT_EQ(column_profile[502], 0.0f);
// Now the same with closing applied. The space in the middle will be closed.
ExtractImageProfileInformation(reduced_color,
image_rect,
gfx::Size(200, 100),
true,
&row_profile,
&column_profile);
EXPECT_GT(column_profile[398], 0);
EXPECT_GT(column_profile[400], 0);
EXPECT_GT(column_profile[402], 0);
EXPECT_EQ(column_profile[299], 0);
EXPECT_EQ(column_profile[502], 0);
EXPECT_EQ(column_profile[399], column_profile[401]);
EXPECT_EQ(column_profile[398], column_profile[402]);
}
TEST_F(ThumbnailContentAnalysisTest, AdjustClippingSizeToAspectRatio) {
// The test will exercise several relations of sizes. Basic invariants
// checked in each case: each dimension in adjusted_size ougth not be greater
// than the source image and not lesser than requested target. Aspect ratio
// of adjusted_size should never be worse than that of computed_size.
gfx::Size target_size(212, 100);
gfx::Size image_size(1000, 2000);
gfx::Size computed_size(420, 200);
gfx::Size adjusted_size = AdjustClippingSizeToAspectRatio(
target_size, image_size, computed_size);
EXPECT_LE(adjusted_size.width(), image_size.width());
EXPECT_LE(adjusted_size.height(), image_size.height());
EXPECT_GE(adjusted_size.width(), target_size.width());
EXPECT_GE(adjusted_size.height(), target_size.height());
EXPECT_LE(AspectDifference(target_size, adjusted_size),
AspectDifference(target_size, computed_size));
// This case is special (and trivial): no change expected.
EXPECT_EQ(computed_size, adjusted_size);
// Computed size is too tall. Adjusted size has to add rows.
computed_size.SetSize(600, 150);
adjusted_size = AdjustClippingSizeToAspectRatio(
target_size, image_size, computed_size);
// Invariant check.
EXPECT_LE(adjusted_size.width(), image_size.width());
EXPECT_LE(adjusted_size.height(), image_size.height());
EXPECT_GE(adjusted_size.width(), target_size.width());
EXPECT_GE(adjusted_size.height(), target_size.height());
EXPECT_LE(AspectDifference(target_size, adjusted_size),
AspectDifference(target_size, computed_size));
// Specific to this case.
EXPECT_EQ(computed_size.width(), adjusted_size.width());
EXPECT_LE(computed_size.height(), adjusted_size.height());
EXPECT_NEAR(
static_cast<float>(target_size.width()) / target_size.height(),
static_cast<float>(adjusted_size.width()) / adjusted_size.height(),
0.02f);
// Computed size is too wide. Adjusted size has to add columns.
computed_size.SetSize(200, 400);
adjusted_size = AdjustClippingSizeToAspectRatio(
target_size, image_size, computed_size);
// Invariant check.
EXPECT_LE(adjusted_size.width(), image_size.width());
EXPECT_LE(adjusted_size.height(), image_size.height());
EXPECT_GE(adjusted_size.width(), target_size.width());
EXPECT_GE(adjusted_size.height(), target_size.height());
EXPECT_LE(AspectDifference(target_size, adjusted_size),
AspectDifference(target_size, computed_size));
EXPECT_NEAR(
static_cast<float>(target_size.width()) / target_size.height(),
static_cast<float>(adjusted_size.width()) / adjusted_size.height(),
0.02f);
target_size.SetSize(416, 205);
image_size.SetSize(1200, 1200);
computed_size.SetSize(900, 300);
adjusted_size = AdjustClippingSizeToAspectRatio(
target_size, image_size, computed_size);
// Invariant check.
EXPECT_LE(adjusted_size.width(), image_size.width());
EXPECT_LE(adjusted_size.height(), image_size.height());
EXPECT_GE(adjusted_size.width(), target_size.width());
EXPECT_GE(adjusted_size.height(), target_size.height());
EXPECT_LE(AspectDifference(target_size, adjusted_size),
AspectDifference(target_size, computed_size));
// Specific to this case.
EXPECT_EQ(computed_size.width(), adjusted_size.width());
EXPECT_LE(computed_size.height(), adjusted_size.height());
EXPECT_NEAR(
static_cast<float>(target_size.width()) / target_size.height(),
static_cast<float>(adjusted_size.width()) / adjusted_size.height(),
0.02f);
target_size.SetSize(416, 205);
image_size.SetSize(1200, 1200);
computed_size.SetSize(300, 300);
adjusted_size = AdjustClippingSizeToAspectRatio(
target_size, image_size, computed_size);
// Invariant check.
EXPECT_LE(adjusted_size.width(), image_size.width());
EXPECT_LE(adjusted_size.height(), image_size.height());
EXPECT_GE(adjusted_size.width(), target_size.width());
EXPECT_GE(adjusted_size.height(), target_size.height());
EXPECT_LE(AspectDifference(target_size, adjusted_size),
AspectDifference(target_size, computed_size));
// Specific to this case.
EXPECT_EQ(computed_size.height(), adjusted_size.height());
EXPECT_LE(computed_size.width(), adjusted_size.width());
EXPECT_NEAR(
static_cast<float>(target_size.width()) / target_size.height(),
static_cast<float>(adjusted_size.width()) / adjusted_size.height(),
0.02f);
computed_size.SetSize(200, 300);
adjusted_size = AdjustClippingSizeToAspectRatio(
target_size, image_size, computed_size);
// Invariant check.
EXPECT_LE(adjusted_size.width(), image_size.width());
EXPECT_LE(adjusted_size.height(), image_size.height());
EXPECT_GE(adjusted_size.width(), target_size.width());
EXPECT_GE(adjusted_size.height(), target_size.height());
EXPECT_LE(AspectDifference(target_size, adjusted_size),
AspectDifference(target_size, computed_size));
// Specific to this case.
EXPECT_EQ(computed_size.height(), adjusted_size.height());
EXPECT_LE(computed_size.width(), adjusted_size.width());
EXPECT_NEAR(
static_cast<float>(target_size.width()) / target_size.height(),
static_cast<float>(adjusted_size.width()) / adjusted_size.height(),
0.02f);
target_size.SetSize(416, 205);
image_size.SetSize(1400, 600);
computed_size.SetSize(300, 300);
adjusted_size = AdjustClippingSizeToAspectRatio(
target_size, image_size, computed_size);
// Invariant check.
EXPECT_LE(adjusted_size.width(), image_size.width());
EXPECT_LE(adjusted_size.height(), image_size.height());
EXPECT_GE(adjusted_size.width(), target_size.width());
EXPECT_GE(adjusted_size.height(), target_size.height());
EXPECT_LE(AspectDifference(target_size, adjusted_size),
AspectDifference(target_size, computed_size));
// Specific to this case.
EXPECT_EQ(computed_size.height(), adjusted_size.height());
EXPECT_LE(computed_size.width(), adjusted_size.width());
EXPECT_NEAR(
static_cast<float>(target_size.width()) / target_size.height(),
static_cast<float>(adjusted_size.width()) / adjusted_size.height(),
0.02f);
computed_size.SetSize(900, 300);
adjusted_size = AdjustClippingSizeToAspectRatio(
target_size, image_size, computed_size);
// Invariant check.
EXPECT_LE(adjusted_size.width(), image_size.width());
EXPECT_LE(adjusted_size.height(), image_size.height());
EXPECT_GE(adjusted_size.width(), target_size.width());
EXPECT_GE(adjusted_size.height(), target_size.height());
EXPECT_LE(AspectDifference(target_size, adjusted_size),
AspectDifference(target_size, computed_size));
// Specific to this case.
EXPECT_LE(computed_size.height(), adjusted_size.height());
EXPECT_NEAR(
static_cast<float>(target_size.width()) / target_size.height(),
static_cast<float>(adjusted_size.width()) / adjusted_size.height(),
0.02f);
}
TEST_F(ThumbnailContentAnalysisTest, AutoSegmentPeaks) {
std::vector<float> profile_info;
EXPECT_EQ(AutoSegmentPeaks(profile_info), std::numeric_limits<float>::max());
profile_info.resize(1000, 1.0f);
EXPECT_EQ(AutoSegmentPeaks(profile_info), 1.0f);
std::srand(42);
std::generate(profile_info.begin(), profile_info.end(), std::rand);
float threshold = AutoSegmentPeaks(profile_info);
EXPECT_GT(threshold, 0); // Not much to expect.
// There should be roughly 50% above and below the threshold.
// Random is not really random thanks to srand, so we can sort-of compare.
int above_count =
std::count_if(profile_info.begin(), profile_info.end(),
[threshold](float value) { return value > threshold; });
EXPECT_GT(above_count, 450); // Not much to expect.
EXPECT_LT(above_count, 550);
for (unsigned i = 0; i < profile_info.size(); ++i) {
float y = std::sin(M_PI * i / 250.0f);
profile_info[i] = y > 0 ? y : 0;
}
threshold = AutoSegmentPeaks(profile_info);
above_count =
std::count_if(profile_info.begin(), profile_info.end(),
[threshold](float value) { return value > threshold; });
EXPECT_LT(above_count, 500); // Negative y expected to fall below threshold.
// Expect two peaks around between 0 and 250 and 500 and 750.
std::vector<bool> thresholded_values(profile_info.size(), false);
std::transform(profile_info.begin(), profile_info.end(),
thresholded_values.begin(),
[threshold](float value) { return value > threshold; });
EXPECT_TRUE(thresholded_values[125]);
EXPECT_TRUE(thresholded_values[625]);
int transitions = 0;
for (unsigned i = 1; i < thresholded_values.size(); ++i) {
if (thresholded_values[i] != thresholded_values[i-1])
transitions++;
}
EXPECT_EQ(transitions, 4); // We have two contiguous peaks. Good going!
}
TEST_F(ThumbnailContentAnalysisTest, ConstrainedProfileSegmentation) {
const size_t kRowCount = 800;
const size_t kColumnCount = 1400;
const gfx::Size target_size(300, 150);
std::vector<float> rows_profile(kRowCount);
std::vector<float> columns_profile(kColumnCount);
std::srand(42);
std::generate(rows_profile.begin(), rows_profile.end(), []() {
return std::rand() / static_cast<float>(RAND_MAX) + 1.f;
});
std::generate(columns_profile.begin(), columns_profile.end(), []() {
return std::rand() / static_cast<float>(RAND_MAX) + 1.f;
});
std::transform(rows_profile.begin() + 300,
rows_profile.begin() + 450,
rows_profile.begin() + 300,
[](float value) { return value + 8.f; });
std::transform(columns_profile.begin() + 400,
columns_profile.begin() + 1000,
columns_profile.begin() + 400,
[](float value) { return value + 10.f; });
// Make sure that threshold falls somewhere reasonable.
float row_threshold = AutoSegmentPeaks(rows_profile);
EXPECT_GT(row_threshold, 1.0f);
EXPECT_LT(row_threshold, 9.0f);
int row_above_count = std::count_if(
rows_profile.begin(),
rows_profile.end(),
[row_threshold](float value) { return value > row_threshold; });
EXPECT_EQ(row_above_count, 150);
float column_threshold = AutoSegmentPeaks(columns_profile);
EXPECT_GT(column_threshold, 1.0f);
EXPECT_LT(column_threshold, 11.0f);
int column_above_count = std::count_if(
columns_profile.begin(),
columns_profile.end(),
[column_threshold](float value) { return value > column_threshold; });
EXPECT_EQ(column_above_count, 600);
std::vector<bool> rows_guide;
std::vector<bool> columns_guide;
ConstrainedProfileSegmentation(
rows_profile, columns_profile, target_size, &rows_guide, &columns_guide);
int row_count = std::count(rows_guide.begin(), rows_guide.end(), true);
int column_count = std::count(
columns_guide.begin(), columns_guide.end(), true);
float expected_aspect =
static_cast<float>(target_size.width()) / target_size.height();
float actual_aspect = static_cast<float>(column_count) / row_count;
EXPECT_GE(1.05f, expected_aspect / actual_aspect);
EXPECT_GE(1.05f, actual_aspect / expected_aspect);
}
TEST_F(ThumbnailContentAnalysisTest, ComputeDecimatedImage) {
gfx::Size image_size(1600, 1200);
gfx::Canvas canvas(image_size, 1.0f, true);
// Make some content we will later want to keep.
canvas.FillRect(gfx::Rect(100, 200, 100, 100), SkColorSetRGB(125, 0, 0));
canvas.FillRect(gfx::Rect(300, 200, 100, 100), SkColorSetRGB(0, 200, 0));
canvas.FillRect(gfx::Rect(500, 200, 100, 100), SkColorSetRGB(0, 0, 225));
canvas.FillRect(gfx::Rect(100, 400, 600, 100), SkColorSetRGB(125, 200, 225));
std::vector<bool> rows(image_size.height(), false);
std::fill_n(rows.begin() + 200, 100, true);
std::fill_n(rows.begin() + 400, 100, true);
std::vector<bool> columns(image_size.width(), false);
std::fill_n(columns.begin() + 100, 100, true);
std::fill_n(columns.begin() + 300, 100, true);
std::fill_n(columns.begin() + 500, 100, true);
SkBitmap source = skia::ReadPixels(canvas.sk_canvas());
SkBitmap result = ComputeDecimatedImage(source, rows, columns);
EXPECT_FALSE(result.empty());
EXPECT_EQ(300, result.width());
EXPECT_EQ(200, result.height());
// The call should have removed all empty spaces.
ASSERT_TRUE(CompareImageFragments(source,
result,
gfx::Size(100, 100),
gfx::Point(100, 200),
gfx::Point(0, 0)));
ASSERT_TRUE(CompareImageFragments(source,
result,
gfx::Size(100, 100),
gfx::Point(300, 200),
gfx::Point(100, 0)));
ASSERT_TRUE(CompareImageFragments(source,
result,
gfx::Size(100, 100),
gfx::Point(500, 200),
gfx::Point(200, 0)));
ASSERT_TRUE(CompareImageFragments(source,
result,
gfx::Size(100, 100),
gfx::Point(100, 400),
gfx::Point(0, 100)));
}
TEST_F(ThumbnailContentAnalysisTest, CreateRetargetedThumbnailImage) {
gfx::Size image_size(1200, 1300);
gfx::Canvas canvas(image_size, 1.0f, true);
// The following will create a 'fake image' consisting of color blocks placed
// on a neutral background. The entire layout is supposed to mimic a
// screenshot of a web page.
// The tested function is supposed to locate the interesing areas in the
// middle.
const int margin_horizontal = 60;
const int margin_vertical = 20;
canvas.FillRect(gfx::Rect(image_size), SkColorSetRGB(200, 210, 210));
const gfx::Rect header_rect(margin_horizontal,
margin_vertical,
image_size.width() - 2 * margin_horizontal,
100);
const gfx::Rect footer_rect(margin_horizontal,
image_size.height() - margin_vertical - 100,
image_size.width() - 2 * margin_horizontal,
100);
const gfx::Rect body_rect(margin_horizontal,
header_rect.bottom() + margin_vertical,
image_size.width() - 2 * margin_horizontal,
footer_rect.y() - header_rect.bottom() -
2 * margin_vertical);
canvas.FillRect(header_rect, SkColorSetRGB(200, 40, 10));
canvas.FillRect(footer_rect, SkColorSetRGB(10, 40, 180));
canvas.FillRect(body_rect, SkColorSetRGB(150, 180, 40));
// 'Fine print' at the bottom.
const int fine_print = 8;
const SkColor print_color = SkColorSetRGB(45, 30, 30);
for (int y = footer_rect.y() + fine_print;
y < footer_rect.bottom() - fine_print;
y += 2 * fine_print) {
for (int x = footer_rect.x() + fine_print;
x < footer_rect.right() - fine_print;
x += 2 * fine_print) {
canvas.DrawRect(gfx::Rect(x, y, fine_print, fine_print), print_color);
}
}
// Blocky content at the top.
const int block_size = header_rect.height() - margin_vertical;
for (int x = header_rect.x() + margin_horizontal;
x < header_rect.right() - block_size;
x += block_size + margin_horizontal) {
const int half_block = block_size / 2 - 5;
const SkColor block_color = SkColorSetRGB(255, 255, 255);
const int y = header_rect.y() + margin_vertical / 2;
int second_col = x + half_block + 10;
int second_row = y + half_block + 10;
canvas.FillRect(gfx::Rect(x, y, half_block, block_size), block_color);
canvas.FillRect(gfx::Rect(second_col, y, half_block, half_block),
block_color);
canvas.FillRect(gfx::Rect(second_col, second_row, half_block, half_block),
block_color);
}
// Now the main body. Mostly text with some 'pictures'.
for (int y = body_rect.y() + fine_print;
y < body_rect.bottom() - fine_print;
y += 2 * fine_print) {
for (int x = body_rect.x() + fine_print;
x < body_rect.right() - fine_print;
x += 2 * fine_print) {
canvas.DrawRect(gfx::Rect(x, y, fine_print, fine_print), print_color);
}
}
for (int line = 0; line < 3; ++line) {
int alignment = line % 2;
const int y = body_rect.y() +
body_rect.height() / 3 * line + margin_vertical;
const int x = body_rect.x() +
alignment * body_rect.width() / 2 + margin_vertical;
gfx::Rect pict_rect(x, y,
body_rect.width() / 2 - 2 * margin_vertical,
body_rect.height() / 3 - 2 * margin_vertical);
canvas.FillRect(pict_rect, SkColorSetRGB(255, 255, 255));
canvas.DrawRect(pict_rect, SkColorSetRGB(0, 0, 0));
}
SkBitmap source = skia::ReadPixels(canvas.sk_canvas());
SkBitmap result = CreateRetargetedThumbnailImage(
source, gfx::Size(424, 264), 2.5);
EXPECT_FALSE(result.empty());
// Given the nature of computation We can't really assert much here about the
// image itself. We know it should have been computed, should be smaller than
// the original and it must not be zero.
EXPECT_LT(result.width(), image_size.width());
EXPECT_LT(result.height(), image_size.height());
int histogram[256] = {};
color_utils::BuildLumaHistogram(result, histogram);
int non_zero_color_count = std::count_if(
histogram, histogram + 256, [](int value) { return value > 0; });
EXPECT_GT(non_zero_color_count, 4);
}
} // namespace thumbnailing_utils