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// Copyright 2019 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 "third_party/blink/renderer/platform/graphics/dark_mode_generic_classifier.h"
#include "third_party/blink/renderer/platform/graphics/darkmode/darkmode_classifier.h"
#include "third_party/blink/renderer/platform/graphics/image.h"
namespace blink {
namespace {
// Decision tree lower and upper thresholds for grayscale and color images.
const float kLowColorCountThreshold[2] = {0.8125, 0.015137};
const float kHighColorCountThreshold[2] = {1, 0.025635};
DarkModeClassification ClassifyUsingDecisionTree(
const DarkModeImageClassifier::Features& features) {
float low_color_count_threshold =
kLowColorCountThreshold[features.is_colorful];
float high_color_count_threshold =
kHighColorCountThreshold[features.is_colorful];
// Very few colors means it's not a photo, apply the filter.
if (features.color_buckets_ratio < low_color_count_threshold)
return DarkModeClassification::kApplyFilter;
// Too many colors means it's probably photorealistic, do not apply it.
if (features.color_buckets_ratio > high_color_count_threshold)
return DarkModeClassification::kDoNotApplyFilter;
// In-between, decision tree cannot give a precise result.
return DarkModeClassification::kNotClassified;
}
// The neural network expects these features to be in a specific order within
// the vector. Do not change the order here without also changing the neural
// network code!
Vector<float> ToVector(const DarkModeImageClassifier::Features& features) {
return {features.is_colorful, features.color_buckets_ratio,
features.transparency_ratio, features.background_ratio,
features.is_svg};
}
} // namespace
DarkModeGenericClassifier::DarkModeGenericClassifier() {}
DarkModeClassification DarkModeGenericClassifier::ClassifyWithFeatures(
const Features& features) {
DarkModeClassification result = ClassifyUsingDecisionTree(features);
// If decision tree cannot decide, we use a neural network to decide whether
// to filter or not based on all the features.
if (result == DarkModeClassification::kNotClassified) {
darkmode_tfnative_model::FixedAllocations nn_temp;
float nn_out;
auto feature_vector = ToVector(features);
darkmode_tfnative_model::Inference(&feature_vector[0], &nn_out, &nn_temp);
result = nn_out > 0 ? DarkModeClassification::kApplyFilter
: DarkModeClassification::kDoNotApplyFilter;
}
return result;
}
DarkModeClassification
DarkModeGenericClassifier::ClassifyUsingDecisionTreeForTesting(
const Features& features) {
return ClassifyUsingDecisionTree(features);
}
} // namespace blink
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