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/*
* Copyright (C) Andrew Helmer 2020.
* Licensed under MIT Open-Source License: see LICENSE.
*
* Implements the most basic algorithm from Christensen et al., Progressive
* Jittered Sampling. There's really no reason to use this, but the code could
* be instructional.
*/
#ifndef SAMPLE_GENERATION_PJ_H_
#define SAMPLE_GENERATION_PJ_H_
#include <algorithm>
#include <cstdlib>
#include <iostream>
#include <memory>
#include <random>
#include <utility>
#include <vector>
#include "util.h"
#include "rng.h"
namespace pmj {
// Progressive jittered samples shouldn't really be used, it's more just a
// learning example.
std::unique_ptr<pmj::Point[]> GetProgJitteredSamples(
const int num_samples, random_gen& rng);
Point RandomSample(
double min_x, double max_x, double min_y, double max_y, random_gen& rng) {
return {UniformRand(min_x, max_x, rng), UniformRand(min_y, max_y, rng)};
}
Point GetSample(
const int x_pos, const int y_pos, const double grid_size, random_gen& rng) {
return RandomSample(x_pos*grid_size, (x_pos+1)*grid_size,
y_pos*grid_size, (y_pos+1)*grid_size, rng);
}
void GenerateSamplesForQuadrant(
const Point& sample,
const int num_samples,
const int n,
const int i,
const int x_pos,
const int y_pos,
const double grid_size,
Point* samples,
random_gen& rng) {
// Generate diagonally opposite.
samples[n+i] = GetSample(x_pos ^ 1, y_pos ^ 1, grid_size, rng);
if (2*n+i >= num_samples) {
return;
}
// Pick one of the two adjacent cells to generate new sample.
int new_x_pos = x_pos;
int new_y_pos = y_pos;
if (UniformRand(0, 1, rng) < 0.5) {
new_x_pos = x_pos ^ 1;
} else {
new_y_pos = y_pos ^ 1;
}
samples[2*n+i] = GetSample(new_x_pos, new_y_pos, grid_size, rng);
// Generate a sample in the diagonal of the previous cell.
if (3*n+i >= num_samples) {
return;
}
samples[3*n+i] = GetSample(new_x_pos ^ 1, new_y_pos ^ 1, grid_size, rng);
}
std::unique_ptr<Point[]> GetProgJitteredSamples(
const int num_samples, random_gen& rng) {
auto samples = std::unique_ptr<Point[]>(new Point[num_samples]());
// Generate first sample randomly.
samples[0] = RandomSample(0, 1, 0, 1, rng);
int n = 1; // Number of samples in previous pass.
int dim = 2; // The number of subquadrants in one dimension.
double grid_size = 0.5; // The subquadrant size in one dimension, 1.0 / dim.
while (n < num_samples) {
for (int i = 0; i < n && n+i < num_samples; i++) {
const auto& sample = samples[i];
int x_pos = sample.x * dim;
int y_pos = sample.y * dim;
GenerateSamplesForQuadrant(
sample, num_samples, n, i, x_pos, y_pos, grid_size, samples.get(), rng);
}
n *= 4;
dim *= 2;
grid_size *= 0.5;
}
return samples;
}
} //namespace pmj
#endif // SAMPLE_GENERATION_PJ_H_
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