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use std::f32::consts::PI;
use std::sync::Arc;
use crate::core::geometry::{Point2f, Vector2f, Vector3f, XYEnum};
use crate::core::pbrt::clamp_t;
use crate::core::pbrt::Float;
use crate::core::pbrt::{INV_2_PI, INV_4_PI, INV_PI, PI_OVER_2, PI_OVER_4};
use crate::core::rng::Rng;
use crate::core::rng::FLOAT_ONE_MINUS_EPSILON;
#[derive(Debug, Default, Clone)]
pub struct Distribution1D {
pub func: Vec<Float>,
pub cdf: Vec<Float>,
pub func_int: Float,
}
impl Distribution1D {
pub fn new(f: Vec<Float>) -> Self {
let n: usize = f.len();
let mut cdf: Vec<Float> = Vec::with_capacity(n + 1);
cdf.push(0.0 as Float);
for i in 1..=n {
let previous: Float = cdf[i - 1];
cdf.push(previous + f[i - 1] / n as Float);
}
let func_int: Float = cdf[n];
if func_int == 0.0 as Float {
for (i, item) in cdf.iter_mut().enumerate().skip(1).take(n) {
*item = i as Float / n as Float;
}
} else {
for item in cdf.iter_mut().skip(1).take(n) {
*item /= func_int;
}
}
Distribution1D {
func: f,
cdf,
func_int,
}
}
pub fn count(&self) -> usize {
self.func.len()
}
pub fn sample_continuous(
&self,
u: Float,
pdf: Option<&mut Float>,
off: Option<&mut usize>,
) -> Float {
let mut first: usize = 0;
let mut len: usize = self.cdf.len();
while len > 0 as usize {
let half: usize = len >> 1;
let middle: usize = first + half;
if self.cdf[middle] <= u {
first = middle + 1;
len -= half + 1;
} else {
len = half;
}
}
let offset: usize = clamp_t(
first as isize - 1_isize,
0 as isize,
self.cdf.len() as isize - 2_isize,
) as usize;
if let Some(off_ref) = off {
*off_ref = offset;
}
let mut du: Float = u - self.cdf[offset];
if (self.cdf[offset + 1] - self.cdf[offset]) > 0.0 as Float {
assert!(self.cdf[offset + 1] > self.cdf[offset]);
du /= self.cdf[offset + 1] - self.cdf[offset];
}
assert!(!du.is_nan());
if let Some(value) = pdf {
if self.func_int > 0.0 as Float {
*value = self.func[offset] / self.func_int;
} else {
*value = 0.0;
}
}
(offset as Float + du) / self.count() as Float
}
pub fn sample_discrete(
&self,
u: Float,
pdf: Option<&mut Float>,
) -> usize {
let mut first: usize = 0;
let mut len: usize = self.cdf.len();
while len > 0 as usize {
let half: usize = len >> 1;
let middle: usize = first + half;
if self.cdf[middle] <= u {
first = middle + 1;
len -= half + 1;
} else {
len = half;
}
}
let offset: usize = clamp_t(
first as isize - 1_isize,
0 as isize,
self.cdf.len() as isize - 2_isize,
) as usize;
if let Some(value) = pdf {
if self.func_int > 0.0 as Float {
*value = self.func[offset] / (self.func_int * self.func.len() as Float);
} else {
*value = 0.0;
}
}
offset
}
pub fn discrete_pdf(&self, index: usize) -> Float {
assert!(index < self.func.len());
self.func[index] / (self.func_int * self.func.len() as Float)
}
}
#[derive(Debug, Default, Clone)]
pub struct Distribution2D {
pub p_conditional_v: Vec<Arc<Distribution1D>>,
pub p_marginal: Arc<Distribution1D>,
}
impl Distribution2D {
pub fn new(func: Vec<Float>, nu: i32, nv: i32) -> Self {
let mut p_conditional_v: Vec<Arc<Distribution1D>> = Vec::with_capacity(nv as usize);
for v in 0..nv {
let f: Vec<Float> = func[(v * nu) as usize..((v + 1) * nu) as usize].to_vec();
p_conditional_v.push(Arc::new(Distribution1D::new(f)));
}
let mut marginal_func: Vec<Float> = Vec::with_capacity(nv as usize);
for v in 0..nv {
marginal_func.push(p_conditional_v[v as usize].func_int);
}
let p_marginal: Arc<Distribution1D> = Arc::new(Distribution1D::new(marginal_func));
Distribution2D {
p_conditional_v,
p_marginal,
}
}
pub fn sample_continuous(&self, u: Point2f, pdf: &mut Float) -> Point2f {
let mut pdfs: [Float; 2] = [0.0 as Float; 2];
let mut v: usize = 0_usize;
let d1: Float =
self.p_marginal
.sample_continuous(u[XYEnum::Y], Some(&mut (pdfs[1])), Some(&mut v));
let d0: Float =
self.p_conditional_v[v].sample_continuous(u[XYEnum::X], Some(&mut (pdfs[0])), None);
*pdf = pdfs[0] * pdfs[1];
Point2f { x: d0, y: d1 }
}
pub fn pdf(&self, p: Point2f) -> Float {
let iu: usize = clamp_t(
(p[XYEnum::X] * self.p_conditional_v[0].count() as Float) as usize,
0_usize,
self.p_conditional_v[0].count() - 1_usize,
);
let iv: usize = clamp_t(
(p[XYEnum::Y] * self.p_marginal.count() as Float) as usize,
0_usize,
self.p_marginal.count() - 1_usize,
);
self.p_conditional_v[iv].func[iu] / self.p_marginal.func_int
}
}
pub fn shuffle<T>(samp: &mut [T], count: i32, n_dimensions: i32, rng: &mut Rng) {
for i in 0..count {
let other: i32 = i + rng.uniform_uint32_bounded((count - i) as u32) as i32;
for j in 0..n_dimensions {
samp.swap(
(n_dimensions * i + j) as usize,
(n_dimensions * other + j) as usize,
);
}
}
}
pub fn cosine_sample_hemisphere(u: &Point2f) -> Vector3f {
let d: Point2f = concentric_sample_disk(u);
let z: Float = (0.0 as Float)
.max(1.0 as Float - d.x * d.x - d.y * d.y)
.sqrt();
Vector3f { x: d.x, y: d.y, z }
}
pub fn cosine_hemisphere_pdf(cos_theta: Float) -> Float {
cos_theta * INV_PI
}
pub fn power_heuristic(nf: u8, f_pdf: Float, ng: u8, g_pdf: Float) -> Float {
let f: Float = nf as Float * f_pdf;
let g: Float = ng as Float * g_pdf;
(f * f) / (f * f + g * g)
}
pub fn stratified_sample_1d(samp: &mut [Float], n_samples: i32, rng: &mut Rng, jitter: bool) {
let inv_n_samples: Float = 1.0 as Float / n_samples as Float;
for i in 0..n_samples {
let delta = if jitter {
rng.uniform_float()
} else {
0.5 as Float
};
samp[i as usize] =
((i as Float + delta) * inv_n_samples as Float).min(FLOAT_ONE_MINUS_EPSILON);
}
}
pub fn stratified_sample_2d(samp: &mut [Point2f], nx: i32, ny: i32, rng: &mut Rng, jitter: bool) {
let dx: Float = 1.0 as Float / nx as Float;
let dy: Float = 1.0 as Float / ny as Float;
let mut samp_idx: usize = 0;
for y in 0..ny {
for x in 0..nx {
let jx = if jitter {
rng.uniform_float()
} else {
0.5 as Float
};
let jy = if jitter {
rng.uniform_float()
} else {
0.5 as Float
};
samp[samp_idx].x = ((x as Float + jx) * dx).min(FLOAT_ONE_MINUS_EPSILON);
samp[samp_idx].y = ((y as Float + jy) * dy).min(FLOAT_ONE_MINUS_EPSILON);
samp_idx += 1;
}
}
}
pub fn latin_hypercube(samples: &mut [Point2f], n_samples: u32, rng: &mut Rng) {
let n_dim: usize = 2;
let inv_n_samples: Float = 1.0 as Float / n_samples as Float;
for i in 0..n_samples {
for j in 0..n_dim {
let sj: Float = (i as Float + (rng.uniform_float())) * inv_n_samples;
if j == 0 {
samples[i as usize].x = sj.min(FLOAT_ONE_MINUS_EPSILON);
} else {
samples[i as usize].y = sj.min(FLOAT_ONE_MINUS_EPSILON);
}
}
}
for i in 0..n_dim {
for j in 0..n_samples {
let other: u32 = j as u32 + rng.uniform_uint32_bounded((n_samples - j) as u32);
if i == 0 {
let tmp = samples[j as usize].x;
samples[j as usize].x = samples[other as usize].x;
samples[other as usize].x = tmp;
} else {
let tmp = samples[j as usize].y;
samples[j as usize].y = samples[other as usize].y;
samples[other as usize].y = tmp;
}
}
}
}
pub fn uniform_sample_hemisphere(u: &Point2f) -> Vector3f {
let z: Float = u[XYEnum::X];
let r: Float = (0.0 as Float).max(1.0 as Float - z * z).sqrt();
let phi: Float = 2.0 as Float * PI * u[XYEnum::Y];
Vector3f {
x: r * phi.cos(),
y: r * phi.sin(),
z,
}
}
pub fn uniform_hemisphere_pdf() -> Float {
INV_2_PI
}
pub fn uniform_sample_sphere(u: Point2f) -> Vector3f {
let z: Float = 1.0 as Float - 2.0 as Float * u[XYEnum::X];
let r: Float = (0.0 as Float).max(1.0 as Float - z * z).sqrt();
let phi: Float = 2.0 as Float * PI * u[XYEnum::Y];
Vector3f {
x: r * phi.cos(),
y: r * phi.sin(),
z,
}
}
pub fn uniform_sphere_pdf() -> Float {
INV_4_PI
}
pub fn concentric_sample_disk(u: &Point2f) -> Point2f {
let u_offset: Point2f = u * 2.0 as Float - Vector2f { x: 1.0, y: 1.0 };
if u_offset.x == 0.0 as Float && u_offset.y == 0.0 as Float {
return Point2f::default();
}
let theta: Float;
let r: Float;
if u_offset.x.abs() > u_offset.y.abs() {
r = u_offset.x;
theta = PI_OVER_4 * (u_offset.y / u_offset.x);
} else {
r = u_offset.y;
theta = PI_OVER_2 - PI_OVER_4 * (u_offset.x / u_offset.y);
}
Point2f {
x: theta.cos(),
y: theta.sin(),
} * r
}
pub fn uniform_cone_pdf(cos_theta_max: Float) -> Float {
1.0 as Float / (2.0 as Float * PI * (1.0 as Float - cos_theta_max))
}
pub fn uniform_sample_cone(u: Point2f, cos_theta_max: Float) -> Vector3f {
let cos_theta: Float = (1.0 as Float - u[XYEnum::X]) + u[XYEnum::X] * cos_theta_max;
let sin_theta: Float = (1.0 as Float - cos_theta * cos_theta).sqrt();
let phi: Float = u[XYEnum::Y] * 2.0 as Float * PI;
Vector3f {
x: phi.cos() * sin_theta,
y: phi.sin() * sin_theta,
z: cos_theta,
}
}