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/*
* Copyright 2011-2021 Blender Foundation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "integrator/adaptive_sampling.h"
#include "util/util_math.h"
CCL_NAMESPACE_BEGIN
AdaptiveSampling::AdaptiveSampling()
{
}
int AdaptiveSampling::align_samples(int start_sample, int num_samples) const
{
if (!use) {
return num_samples;
}
/*
* The naive implementation goes as following:
*
* int count = 1;
* while (!need_filter(start_sample + count - 1) && count < num_samples) {
* ++count;
* }
* return count;
*/
/* 0-based sample index at which first filtering will happen. */
const int first_filter_sample = (min_samples + 1) | (adaptive_step - 1);
/* Allow as many samples as possible until the first filter sample. */
if (start_sample + num_samples <= first_filter_sample) {
return num_samples;
}
const int next_filter_sample = max(first_filter_sample, start_sample | (adaptive_step - 1));
const int num_samples_until_filter = next_filter_sample - start_sample + 1;
return min(num_samples_until_filter, num_samples);
}
bool AdaptiveSampling::need_filter(int sample) const
{
if (!use) {
return false;
}
if (sample <= min_samples) {
return false;
}
return (sample & (adaptive_step - 1)) == (adaptive_step - 1);
}
CCL_NAMESPACE_END
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