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// Copyright 2025 The Chromium Authors
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.
#include "components/permissions/prediction_service/permissions_aiv3_encoder.h"
#include <array>
#include <vector>
#include "skia/ext/image_operations.h"
#include "third_party/tflite_support/src/tensorflow_lite_support/cc/task/core/task_utils.h"
namespace permissions {
namespace {
using ModelInput = PermissionsAiv3Encoder::ModelInput;
using ModelOutput = PermissionsAiv3Encoder::ModelOutput;
using ::tflite::task::core::PopulateTensor;
PermissionRequestRelevance ConvertToRelevance(
float val,
const std::array<float, 4>& thresholds) {
// Empirically determined thresholds, that map to relevance enum vals as
// follows:
// val < thr[0] -> VeryLow
// ...
// val < thr[4] -> High
// val >= thr[4] -> VeryHigh
for (size_t i = 0; i < thresholds.size(); ++i) {
if (val < thresholds[i]) {
return static_cast<PermissionRequestRelevance>(i + 1);
}
}
return PermissionRequestRelevance::kVeryHigh;
}
} // namespace
const int PermissionsAiv3Encoder::kModelInputWidth = 64;
const int PermissionsAiv3Encoder::kModelInputHeight = 64;
bool PermissionsAiv3Encoder::Preprocess(
const std::vector<TfLiteTensor*>& input_tensors,
const ModelInput& input) {
// TODO(crbug.com/405095664): Figure out if resize_best is fast enough to deal
// with too big/small inputs.
SkBitmap resized =
skia::ImageOperations::Resize(input, skia::ImageOperations::RESIZE_BEST,
kModelInputWidth, kModelInputHeight);
if (resized.drawsNothing()) {
return false;
}
std::array<float, kModelInputHeight * kModelInputWidth * 3> data;
int index = 0;
for (int h = 0; h < resized.height(); ++h) {
for (int w = 0; w < resized.width(); ++w) {
SkColor color = resized.getColor(h, w);
// We normalize the pixel values to be in between 0 and 1.
// TODO(crbug.com/405095664): We need to investigate if this is the
// correct way to fill the tensors data;
data[index++] = static_cast<float>(SkColorGetR(color)) / 255.0f;
data[index++] = static_cast<float>(SkColorGetG(color)) / 255.0f;
data[index++] = static_cast<float>(SkColorGetB(color)) / 255.0f;
}
}
if (!PopulateTensor<float>(data.data(), data.size(), input_tensors[0]).ok()) {
return false;
}
return true;
}
std::optional<ModelOutput> PermissionsAiv3Encoder::Postprocess(
const std::vector<const TfLiteTensor*>& output_tensors) {
DCHECK(request_type_ == RequestType::kNotifications ||
request_type_ == RequestType::kGeolocation);
// TODO(crbug.com/405095664): should be fetched via the model metadata proto
// as soon as we have this.
static constexpr std::array<float, 4> geolocation_thresholds = {0.2f, 0.4f,
0.5f, 0.65f};
static constexpr std::array<float, 4> notification_thresholds = {0.2f, 0.4f,
0.7f, 0.84f};
std::vector<float> data;
if (!tflite::task::core::PopulateVector<float>(output_tensors[0], &data)
.ok()) {
return std::nullopt;
}
return ConvertToRelevance(data[0],
request_type_ == RequestType::kNotifications
? notification_thresholds
: geolocation_thresholds);
}
} // namespace permissions
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