,
u: f32,
v: f32,
) -> Option {
if ![u, v].iter().all(|c| (0.0..=1.0).contains(c)) {
return None;
}
let (w, h) = img.dimensions();
if w == 0 || h == 0 {
return None;
}
let ui = w as f32 * u - 0.5;
let vi = h as f32 * v - 0.5;
interpolate_bilinear(
img,
ui.max(0.).min((w - 1) as f32),
vi.max(0.).min((h - 1) as f32),
)
}
/// Sample from an image using coordinates in [0, 1], taking the nearest coordinate.
pub fn sample_nearest(
img: &impl GenericImageView,
u: f32,
v: f32,
) -> Option {
if ![u, v].iter().all(|c| (0.0..=1.0).contains(c)) {
return None;
}
let (w, h) = img.dimensions();
let ui = w as f32 * u - 0.5;
let ui = ui.max(0.).min((w.saturating_sub(1)) as f32);
let vi = h as f32 * v - 0.5;
let vi = vi.max(0.).min((h.saturating_sub(1)) as f32);
interpolate_nearest(img, ui, vi)
}
/// Sample from an image using coordinates in [0, w-1] and [0, h-1], taking the
/// nearest pixel.
///
/// Coordinates outside the image bounds will return `None`, however the
/// behavior for points within half a pixel of the image bounds may change in
/// the future.
pub fn interpolate_nearest(
img: &impl GenericImageView,
x: f32,
y: f32,
) -> Option {
let (w, h) = img.dimensions();
if w == 0 || h == 0 {
return None;
}
if !(0.0..=((w - 1) as f32)).contains(&x) {
return None;
}
if !(0.0..=((h - 1) as f32)).contains(&y) {
return None;
}
Some(img.get_pixel(x.round() as u32, y.round() as u32))
}
/// Linearly sample from an image using coordinates in [0, w-1] and [0, h-1].
pub fn interpolate_bilinear(
img: &impl GenericImageView,
x: f32,
y: f32,
) -> Option {
// assumption needed for correctness of pixel creation
assert!(P::CHANNEL_COUNT <= 4);
let (w, h) = img.dimensions();
if w == 0 || h == 0 {
return None;
}
if !(0.0..=((w - 1) as f32)).contains(&x) {
return None;
}
if !(0.0..=((h - 1) as f32)).contains(&y) {
return None;
}
// keep these as integers, for fewer FLOPs
let uf = x.floor() as u32;
let vf = y.floor() as u32;
let uc = (uf + 1).min(w - 1);
let vc = (vf + 1).min(h - 1);
// clamp coords to the range of the image
let mut sxx = [[0.; 4]; 4];
// do not use Array::map, as it can be slow with high stack usage,
// for [[f32; 4]; 4].
// convert samples to f32
// currently rgba is the largest one,
// so just store as many items as necessary,
// because there's not a simple way to be generic over all of them.
let mut compute = |u: u32, v: u32, i| {
let s = img.get_pixel(u, v);
for (j, c) in s.channels().iter().enumerate() {
sxx[j][i] = c.to_f32().unwrap();
}
s
};
// hacky reuse since cannot construct a generic Pixel
let mut out: P = compute(uf, vf, 0);
compute(uf, vc, 1);
compute(uc, vf, 2);
compute(uc, vc, 3);
// weights, the later two are independent from the first 2 for better vectorization.
let ufw = x - uf as f32;
let vfw = y - vf as f32;
let ucw = (uf + 1) as f32 - x;
let vcw = (vf + 1) as f32 - y;
// https://en.wikipedia.org/wiki/Bilinear_interpolation#Weighted_mean
// the distance between pixels is 1 so there is no denominator
let wff = ucw * vcw;
let wfc = ucw * vfw;
let wcf = ufw * vcw;
let wcc = ufw * vfw;
// was originally assert, but is actually not a cheap computation
debug_assert!(f32::abs((wff + wfc + wcf + wcc) - 1.) < 1e-3);
// hack to see if primitive is an integer or a float
let is_float = P::Subpixel::DEFAULT_MAX_VALUE.to_f32().unwrap() == 1.0;
for (i, c) in out.channels_mut().iter_mut().enumerate() {
let v = wff * sxx[i][0] + wfc * sxx[i][1] + wcf * sxx[i][2] + wcc * sxx[i][3];
// this rounding may introduce quantization errors,
// Specifically what is meant is that many samples may deviate
// from the mean value of the originals, but it's not possible to fix that.
*c = ::from(if is_float { v } else { v.round() }).unwrap_or({
if v < 0.0 {
P::Subpixel::DEFAULT_MIN_VALUE
} else {
P::Subpixel::DEFAULT_MAX_VALUE
}
});
}
Some(out)
}
// Sample the columns of the supplied image using the provided filter.
// The width of the image remains unchanged.
// ```new_height``` is the desired height of the new image
// ```filter``` is the filter to use for sampling.
// The return value is not necessarily Rgba, the underlying order of channels in ```image``` is
// preserved.
//
// Note: if an empty image is passed in, panics unless the image is truly empty.
fn vertical_sample(image: &I, new_height: u32, filter: &mut Filter) -> Rgba32FImage
where
I: GenericImageView,
P: Pixel + 'static,
S: Primitive + 'static,
{
let (width, height) = image.dimensions();
// This is protection against a regression in memory usage such as #2340. Since the strategy to
// deal with it depends on the caller it is a precondition of this function.
assert!(
// Checks the implication: (height == 0) -> (width == 0)
height != 0 || width == 0,
"Unexpected prior allocation size. This case should have been handled by the caller"
);
let mut out = ImageBuffer::new(width, new_height);
out.copy_color_space_from(&image.buffer_with_dimensions(0, 0));
let mut ws = Vec::new();
let ratio = height as f32 / new_height as f32;
let sratio = if ratio < 1.0 { 1.0 } else { ratio };
let src_support = filter.support * sratio;
for outy in 0..new_height {
// For an explanation of this algorithm, see the comments
// in horizontal_sample.
let inputy = (outy as f32 + 0.5) * ratio;
let left = (inputy - src_support).floor() as i64;
let left = clamp(left, 0, >::from(height) - 1) as u32;
let right = (inputy + src_support).ceil() as i64;
let right = clamp(
right,
>::from(left) + 1,
>::from(height),
) as u32;
let inputy = inputy - 0.5;
ws.clear();
let mut sum = 0.0;
for i in left..right {
let w = (filter.kernel)((i as f32 - inputy) / sratio);
ws.push(w);
sum += w;
}
for w in ws.iter_mut() {
*w /= sum;
}
for x in 0..width {
let mut t = (0.0, 0.0, 0.0, 0.0);
for (i, w) in ws.iter().enumerate() {
let p = image.get_pixel(x, left + i as u32);
#[allow(deprecated)]
let (k1, k2, k3, k4) = p.channels4();
let vec: (f32, f32, f32, f32) = (
NumCast::from(k1).unwrap(),
NumCast::from(k2).unwrap(),
NumCast::from(k3).unwrap(),
NumCast::from(k4).unwrap(),
);
t.0 += vec.0 * w;
t.1 += vec.1 * w;
t.2 += vec.2 * w;
t.3 += vec.3 * w;
}
#[allow(deprecated)]
// This is not necessarily Rgba.
let t = Pixel::from_channels(t.0, t.1, t.2, t.3);
out.put_pixel(x, outy, t);
}
}
out
}
/// Local struct for keeping track of pixel sums for fast thumbnail averaging
struct ThumbnailSum(S::Larger, S::Larger, S::Larger, S::Larger);
impl ThumbnailSum {
fn zeroed() -> Self {
ThumbnailSum(
S::Larger::zero(),
S::Larger::zero(),
S::Larger::zero(),
S::Larger::zero(),
)
}
fn sample_val(val: S) -> S::Larger {
::from(val).unwrap()
}
fn add_pixel>(&mut self, pixel: P) {
#[allow(deprecated)]
let pixel = pixel.channels4();
self.0 += Self::sample_val(pixel.0);
self.1 += Self::sample_val(pixel.1);
self.2 += Self::sample_val(pixel.2);
self.3 += Self::sample_val(pixel.3);
}
}
/// Resize the supplied image to the specific dimensions.
///
/// For downscaling, this method uses a fast integer algorithm where each source pixel contributes
/// to exactly one target pixel. May give aliasing artifacts if new size is close to old size.
///
/// In case the current width is smaller than the new width or similar for the height, another
/// strategy is used instead. For each pixel in the output, a rectangular region of the input is
/// determined, just as previously. But when no input pixel is part of this region, the nearest
/// pixels are interpolated instead.
///
/// For speed reasons, all interpolation is performed linearly over the colour values. It will not
/// take the pixel colour spaces into account.
pub fn thumbnail(image: &I, new_width: u32, new_height: u32) -> ImageBuffer>
where
I: GenericImageView,
P: Pixel + 'static,
S: Primitive + Enlargeable + 'static,
{
let (width, height) = image.dimensions();
let mut out = image.buffer_with_dimensions(new_width, new_height);
if height == 0 || width == 0 {
return out;
}
let x_ratio = width as f32 / new_width as f32;
let y_ratio = height as f32 / new_height as f32;
for outy in 0..new_height {
let bottomf = outy as f32 * y_ratio;
let topf = bottomf + y_ratio;
let bottom = clamp(bottomf.ceil() as u32, 0, height - 1);
let top = clamp(topf.ceil() as u32, bottom, height);
for outx in 0..new_width {
let leftf = outx as f32 * x_ratio;
let rightf = leftf + x_ratio;
let left = clamp(leftf.ceil() as u32, 0, width - 1);
let right = clamp(rightf.ceil() as u32, left, width);
let avg = if bottom != top && left != right {
thumbnail_sample_block(image, left, right, bottom, top)
} else if bottom != top {
// && left == right
// In the first column we have left == 0 and right > ceil(y_scale) > 0 so this
// assertion can never trigger.
debug_assert!(
left > 0 && right > 0,
"First output column must have corresponding pixels"
);
let fraction_horizontal = (leftf.fract() + rightf.fract()) / 2.;
thumbnail_sample_fraction_horizontal(
image,
right - 1,
fraction_horizontal,
bottom,
top,
)
} else if left != right {
// && bottom == top
// In the first line we have bottom == 0 and top > ceil(x_scale) > 0 so this
// assertion can never trigger.
debug_assert!(
bottom > 0 && top > 0,
"First output row must have corresponding pixels"
);
let fraction_vertical = (topf.fract() + bottomf.fract()) / 2.;
thumbnail_sample_fraction_vertical(image, left, right, top - 1, fraction_vertical)
} else {
// bottom == top && left == right
let fraction_horizontal = (topf.fract() + bottomf.fract()) / 2.;
let fraction_vertical = (leftf.fract() + rightf.fract()) / 2.;
thumbnail_sample_fraction_both(
image,
right - 1,
fraction_horizontal,
top - 1,
fraction_vertical,
)
};
#[allow(deprecated)]
let pixel = Pixel::from_channels(avg.0, avg.1, avg.2, avg.3);
out.put_pixel(outx, outy, pixel);
}
}
out
}
/// Get a pixel for a thumbnail where the input window encloses at least a full pixel.
fn thumbnail_sample_block(
image: &I,
left: u32,
right: u32,
bottom: u32,
top: u32,
) -> (S, S, S, S)
where
I: GenericImageView,
P: Pixel,
S: Primitive + Enlargeable,
{
let mut sum = ThumbnailSum::zeroed();
for y in bottom..top {
for x in left..right {
let k = image.get_pixel(x, y);
sum.add_pixel(k);
}
}
let n = ::from((right - left) * (top - bottom)).unwrap();
let round = ::from(n / NumCast::from(2).unwrap()).unwrap();
(
S::clamp_from((sum.0 + round) / n),
S::clamp_from((sum.1 + round) / n),
S::clamp_from((sum.2 + round) / n),
S::clamp_from((sum.3 + round) / n),
)
}
/// Get a thumbnail pixel where the input window encloses at least a vertical pixel.
fn thumbnail_sample_fraction_horizontal(
image: &I,
left: u32,
fraction_horizontal: f32,
bottom: u32,
top: u32,
) -> (S, S, S, S)
where
I: GenericImageView,
P: Pixel,
S: Primitive + Enlargeable,
{
let fract = fraction_horizontal;
let mut sum_left = ThumbnailSum::zeroed();
let mut sum_right = ThumbnailSum::zeroed();
for x in bottom..top {
let k_left = image.get_pixel(left, x);
sum_left.add_pixel(k_left);
let k_right = image.get_pixel(left + 1, x);
sum_right.add_pixel(k_right);
}
// Now we approximate: left/n*(1-fract) + right/n*fract
let fact_right = fract / ((top - bottom) as f32);
let fact_left = (1. - fract) / ((top - bottom) as f32);
let mix_left_and_right = |leftv: S::Larger, rightv: S::Larger| {
::from(
fact_left * leftv.to_f32().unwrap() + fact_right * rightv.to_f32().unwrap(),
)
.expect("Average sample value should fit into sample type")
};
(
mix_left_and_right(sum_left.0, sum_right.0),
mix_left_and_right(sum_left.1, sum_right.1),
mix_left_and_right(sum_left.2, sum_right.2),
mix_left_and_right(sum_left.3, sum_right.3),
)
}
/// Get a thumbnail pixel where the input window encloses at least a horizontal pixel.
fn thumbnail_sample_fraction_vertical(
image: &I,
left: u32,
right: u32,
bottom: u32,
fraction_vertical: f32,
) -> (S, S, S, S)
where
I: GenericImageView,
P: Pixel,
S: Primitive + Enlargeable,
{
let fract = fraction_vertical;
let mut sum_bot = ThumbnailSum::zeroed();
let mut sum_top = ThumbnailSum::zeroed();
for x in left..right {
let k_bot = image.get_pixel(x, bottom);
sum_bot.add_pixel(k_bot);
let k_top = image.get_pixel(x, bottom + 1);
sum_top.add_pixel(k_top);
}
// Now we approximate: bot/n*fract + top/n*(1-fract)
let fact_top = fract / ((right - left) as f32);
let fact_bot = (1. - fract) / ((right - left) as f32);
let mix_bot_and_top = |botv: S::Larger, topv: S::Larger| {
::from(fact_bot * botv.to_f32().unwrap() + fact_top * topv.to_f32().unwrap())
.expect("Average sample value should fit into sample type")
};
(
mix_bot_and_top(sum_bot.0, sum_top.0),
mix_bot_and_top(sum_bot.1, sum_top.1),
mix_bot_and_top(sum_bot.2, sum_top.2),
mix_bot_and_top(sum_bot.3, sum_top.3),
)
}
/// Get a single pixel for a thumbnail where the input window does not enclose any full pixel.
fn thumbnail_sample_fraction_both(
image: &I,
left: u32,
fraction_vertical: f32,
bottom: u32,
fraction_horizontal: f32,
) -> (S, S, S, S)
where
I: GenericImageView,
P: Pixel,
S: Primitive + Enlargeable,
{
#[allow(deprecated)]
let k_bl = image.get_pixel(left, bottom).channels4();
#[allow(deprecated)]
let k_tl = image.get_pixel(left, bottom + 1).channels4();
#[allow(deprecated)]
let k_br = image.get_pixel(left + 1, bottom).channels4();
#[allow(deprecated)]
let k_tr = image.get_pixel(left + 1, bottom + 1).channels4();
let frac_v = fraction_vertical;
let frac_h = fraction_horizontal;
let fact_tr = frac_v * frac_h;
let fact_tl = frac_v * (1. - frac_h);
let fact_br = (1. - frac_v) * frac_h;
let fact_bl = (1. - frac_v) * (1. - frac_h);
let mix = |br: S, tr: S, bl: S, tl: S| {
::from(
fact_br * br.to_f32().unwrap()
+ fact_tr * tr.to_f32().unwrap()
+ fact_bl * bl.to_f32().unwrap()
+ fact_tl * tl.to_f32().unwrap(),
)
.expect("Average sample value should fit into sample type")
};
(
mix(k_br.0, k_tr.0, k_bl.0, k_tl.0),
mix(k_br.1, k_tr.1, k_bl.1, k_tl.1),
mix(k_br.2, k_tr.2, k_bl.2, k_tl.2),
mix(k_br.3, k_tr.3, k_bl.3, k_tl.3),
)
}
/// Perform a 3x3 box filter on the supplied image.
///
/// # Arguments:
///
/// * `image` - source image.
/// * `kernel` - is an array of the filter weights of length 9.
///
/// This method typically assumes that the input is scene-linear light.
/// If it is not, color distortion may occur.
pub fn filter3x3(image: &I, kernel: &[f32]) -> ImageBuffer>
where
I: GenericImageView,
P: Pixel + 'static,
S: Primitive + 'static,
{
// The kernel's input positions relative to the current pixel.
let taps: &[(isize, isize)] = &[
(-1, -1),
(0, -1),
(1, -1),
(-1, 0),
(0, 0),
(1, 0),
(-1, 1),
(0, 1),
(1, 1),
];
let (width, height) = image.dimensions();
let mut out = image.buffer_like();
let max = S::DEFAULT_MAX_VALUE;
let max: f32 = NumCast::from(max).unwrap();
#[allow(clippy::redundant_guards)]
let sum = match kernel.iter().fold(0.0, |s, &item| s + item) {
x if x == 0.0 => 1.0,
sum => sum,
};
let sum = (sum, sum, sum, sum);
for y in 1..height - 1 {
for x in 1..width - 1 {
let mut t = (0.0, 0.0, 0.0, 0.0);
// TODO: There is no need to recalculate the kernel for each pixel.
// Only a subtract and addition is needed for pixels after the first
// in each row.
for (&k, &(a, b)) in kernel.iter().zip(taps.iter()) {
let k = (k, k, k, k);
let x0 = x as isize + a;
let y0 = y as isize + b;
let p = image.get_pixel(x0 as u32, y0 as u32);
#[allow(deprecated)]
let (k1, k2, k3, k4) = p.channels4();
let vec: (f32, f32, f32, f32) = (
NumCast::from(k1).unwrap(),
NumCast::from(k2).unwrap(),
NumCast::from(k3).unwrap(),
NumCast::from(k4).unwrap(),
);
t.0 += vec.0 * k.0;
t.1 += vec.1 * k.1;
t.2 += vec.2 * k.2;
t.3 += vec.3 * k.3;
}
let (t1, t2, t3, t4) = (t.0 / sum.0, t.1 / sum.1, t.2 / sum.2, t.3 / sum.3);
#[allow(deprecated)]
let t = Pixel::from_channels(
NumCast::from(clamp(t1, 0.0, max)).unwrap(),
NumCast::from(clamp(t2, 0.0, max)).unwrap(),
NumCast::from(clamp(t3, 0.0, max)).unwrap(),
NumCast::from(clamp(t4, 0.0, max)).unwrap(),
);
out.put_pixel(x, y, t);
}
}
out
}
/// Resize the supplied image to the specified dimensions.
///
/// # Arguments:
///
/// * `nwidth` - new image width.
/// * `nheight` - new image height.
/// * `filter` - is the sampling filter to use, see [FilterType] for mor information.
///
/// This method assumes alpha pre-multiplication for images that contain non-constant alpha.
///
/// This method typically assumes that the input is scene-linear light.
/// If it is not, color distortion may occur.
pub fn resize(
image: &I,
nwidth: u32,
nheight: u32,
filter: FilterType,
) -> ImageBuffer::Subpixel>>
where
I::Pixel: 'static,
::Subpixel: 'static,
{
// Check if there is nothing to sample from.
let is_empty = {
let (width, height) = image.dimensions();
width == 0 || height == 0
};
if is_empty {
return image.buffer_with_dimensions(nwidth, nheight);
}
// check if the new dimensions are the same as the old. if they are, make a copy instead of resampling
if (nwidth, nheight) == image.dimensions() {
let mut tmp = image.buffer_like();
tmp.copy_from(image, 0, 0).unwrap();
return tmp;
}
let mut method = match filter {
FilterType::Nearest => Filter {
kernel: Box::new(box_kernel),
support: 0.0,
},
FilterType::Triangle => Filter {
kernel: Box::new(triangle_kernel),
support: 1.0,
},
FilterType::CatmullRom => Filter {
kernel: Box::new(catmullrom_kernel),
support: 2.0,
},
FilterType::Gaussian => Filter {
kernel: Box::new(gaussian_kernel),
support: 3.0,
},
FilterType::Lanczos3 => Filter {
kernel: Box::new(lanczos3_kernel),
support: 3.0,
},
};
// Note: tmp is not necessarily actually Rgba
let tmp: Rgba32FImage = vertical_sample(image, nheight, &mut method);
horizontal_sample(&tmp, nwidth, &mut method)
}
/// Performs a Gaussian blur on the supplied image.
///
/// # Arguments
///
/// - `sigma` - gaussian bell flattening level.
///
/// Use [`crate::imageops::fast_blur()`] for a faster but less
/// accurate version.
/// This method assumes alpha pre-multiplication for images that contain non-constant alpha.
/// This method typically assumes that the input is scene-linear light.
/// If it is not, color distortion may occur.
pub fn blur(
image: &I,
sigma: f32,
) -> ImageBuffer::Subpixel>>
where
I::Pixel: 'static,
{
gaussian_blur_indirect(
image,
GaussianBlurParameters::new_from_sigma(if sigma == 0.0 { 0.8 } else { sigma }),
)
}
/// Performs a Gaussian blur on the supplied image.
///
/// # Arguments
///
/// - `parameters` - see [GaussianBlurParameters] for more info.
///
/// This method assumes alpha pre-multiplication for images that contain non-constant alpha.
/// This method typically assumes that the input is scene-linear light.
/// If it is not, color distortion may occur.
pub fn blur_advanced(
image: &I,
parameters: GaussianBlurParameters,
) -> ImageBuffer::Subpixel>>
where
I::Pixel: 'static,
{
gaussian_blur_indirect(image, parameters)
}
fn get_gaussian_kernel_1d(width: usize, sigma: f32) -> Vec {
let mut sum_norm: f32 = 0f32;
let mut kernel = vec![0f32; width];
let scale = 1f32 / (f32::sqrt(2f32 * f32::consts::PI) * sigma);
let mean = (width / 2) as f32;
for (x, weight) in kernel.iter_mut().enumerate() {
let new_weight = f32::exp(-0.5f32 * f32::powf((x as f32 - mean) / sigma, 2.0f32)) * scale;
*weight = new_weight;
sum_norm += new_weight;
}
if sum_norm != 0f32 {
let sum_scale = 1f32 / sum_norm;
for weight in &mut kernel {
*weight = weight.mul(sum_scale);
}
}
kernel
}
/// Holds analytical gaussian blur representation
#[derive(Copy, Clone, PartialOrd, PartialEq)]
pub struct GaussianBlurParameters {
/// X-axis kernel, must be odd
x_axis_kernel_size: u32,
/// X-axis sigma, must > 0, not subnormal, and not NaN
x_axis_sigma: f32,
/// Y-axis kernel, must be odd
y_axis_kernel_size: u32,
/// Y-axis sigma, must > 0, not subnormal, and not NaN
y_axis_sigma: f32,
}
impl GaussianBlurParameters {
/// Built-in smoothing kernel with size 3.
pub const SMOOTHING_3: GaussianBlurParameters = GaussianBlurParameters {
x_axis_kernel_size: 3,
x_axis_sigma: 0.8,
y_axis_kernel_size: 3,
y_axis_sigma: 0.8,
};
/// Built-in smoothing kernel with size 5.
pub const SMOOTHING_5: GaussianBlurParameters = GaussianBlurParameters {
x_axis_kernel_size: 5,
x_axis_sigma: 1.1,
y_axis_kernel_size: 5,
y_axis_sigma: 1.1,
};
/// Built-in smoothing kernel with size 7.
pub const SMOOTHING_7: GaussianBlurParameters = GaussianBlurParameters {
x_axis_kernel_size: 7,
x_axis_sigma: 1.4,
y_axis_kernel_size: 7,
y_axis_sigma: 1.4,
};
/// Creates a new parameters set from radius only.
pub fn new_from_radius(radius: f32) -> GaussianBlurParameters {
// Previous implementation was allowing passing 0 so we'll allow here also.
assert!(radius >= 0.0);
if radius != 0. {
assert!(
radius.is_normal(),
"Radius do not allow infinities, NaNs or subnormals"
);
}
GaussianBlurParameters::new_from_kernel_size(radius * 2. + 1.)
}
/// Creates a new parameters set from kernel size only.
///
/// Kernel size will be rounded to nearest odd, and used with fraction
/// to compute accurate required sigma.
pub fn new_from_kernel_size(kernel_size: f32) -> GaussianBlurParameters {
assert!(
kernel_size > 0.,
"Kernel size do not allow infinities, zeros, NaNs or subnormals or negatives"
);
assert!(
kernel_size.is_normal(),
"Kernel size do not allow infinities, zeros, NaNs or subnormals or negatives"
);
let i_kernel_size = GaussianBlurParameters::round_to_nearest_odd(kernel_size);
assert_ne!(i_kernel_size % 2, 0, "Kernel size must be odd");
let v_sigma = GaussianBlurParameters::sigma_size(kernel_size);
GaussianBlurParameters {
x_axis_kernel_size: i_kernel_size,
x_axis_sigma: v_sigma,
y_axis_kernel_size: i_kernel_size,
y_axis_sigma: v_sigma,
}
}
/// Creates a new anisotropic parameter set from kernel sizes
///
/// Kernel size will be rounded to nearest odd, and used with fraction
/// to compute accurate required sigma.
pub fn new_anisotropic_kernel_size(
x_axis_kernel_size: f32,
y_axis_kernel_size: f32,
) -> GaussianBlurParameters {
assert!(
x_axis_kernel_size > 0.,
"Kernel size do not allow infinities, zeros, NaNs or subnormals or negatives"
);
assert!(
y_axis_kernel_size.is_normal(),
"Kernel size do not allow infinities, zeros, NaNs or subnormals or negatives"
);
assert!(
y_axis_kernel_size > 0.,
"Kernel size do not allow infinities, zeros, NaNs or subnormals or negatives"
);
assert!(
y_axis_kernel_size.is_normal(),
"Kernel size do not allow infinities, zeros, NaNs or subnormals or negatives"
);
let x_kernel_size = GaussianBlurParameters::round_to_nearest_odd(x_axis_kernel_size);
assert_ne!(x_kernel_size % 2, 0, "Kernel size must be odd");
let y_kernel_size = GaussianBlurParameters::round_to_nearest_odd(y_axis_kernel_size);
assert_ne!(y_kernel_size % 2, 0, "Kernel size must be odd");
let x_sigma = GaussianBlurParameters::sigma_size(x_axis_kernel_size);
let y_sigma = GaussianBlurParameters::sigma_size(y_axis_kernel_size);
GaussianBlurParameters {
x_axis_kernel_size: x_kernel_size,
x_axis_sigma: x_sigma,
y_axis_kernel_size: y_kernel_size,
y_axis_sigma: y_sigma,
}
}
/// Creates a new parameters set from sigma only
pub fn new_from_sigma(sigma: f32) -> GaussianBlurParameters {
assert!(
sigma.is_normal(),
"Sigma cannot be NaN, Infinities, subnormal or zero"
);
assert!(sigma > 0.0, "Sigma must be positive");
let kernel_size = GaussianBlurParameters::kernel_size_from_sigma(sigma);
GaussianBlurParameters {
x_axis_kernel_size: kernel_size,
x_axis_sigma: sigma,
y_axis_kernel_size: kernel_size,
y_axis_sigma: sigma,
}
}
#[inline]
fn round_to_nearest_odd(x: f32) -> u32 {
let n = x.round() as u32;
if n % 2 != 0 {
n
} else {
let lower = n - 1;
let upper = n + 1;
let dist_lower = (x - lower as f32).abs();
let dist_upper = (x - upper as f32).abs();
if dist_lower <= dist_upper {
lower
} else {
upper
}
}
}
fn sigma_size(kernel_size: f32) -> f32 {
let safe_kernel_size = if kernel_size <= 1. { 0.8 } else { kernel_size };
0.3 * ((safe_kernel_size - 1.) * 0.5 - 1.) + 0.8
}
fn kernel_size_from_sigma(sigma: f32) -> u32 {
let possible_size = (((((sigma - 0.8) / 0.3) + 1.) * 2.) + 1.).max(3.) as u32;
if possible_size % 2 == 0 {
return possible_size + 1;
}
possible_size
}
}
pub(crate) fn gaussian_blur_dyn_image(
image: &DynamicImage,
parameters: GaussianBlurParameters,
) -> DynamicImage {
let x_axis_kernel = get_gaussian_kernel_1d(
parameters.x_axis_kernel_size as usize,
parameters.x_axis_sigma,
);
let y_axis_kernel = get_gaussian_kernel_1d(
parameters.y_axis_kernel_size as usize,
parameters.y_axis_sigma,
);
let filter_image_size = FilterImageSize {
width: image.width() as usize,
height: image.height() as usize,
};
let mut target = match image {
DynamicImage::ImageLuma8(img) => {
let mut dest_image = vec![0u8; img.len()];
filter_2d_sep_plane(
img.as_raw(),
&mut dest_image,
filter_image_size,
&x_axis_kernel,
&y_axis_kernel,
)
.unwrap();
DynamicImage::ImageLuma8(
GrayImage::from_raw(img.width(), img.height(), dest_image).unwrap(),
)
}
DynamicImage::ImageLumaA8(img) => {
let mut dest_image = vec![0u8; img.len()];
filter_2d_sep_la(
img.as_raw(),
&mut dest_image,
filter_image_size,
&x_axis_kernel,
&y_axis_kernel,
)
.unwrap();
DynamicImage::ImageLumaA8(
GrayAlphaImage::from_raw(img.width(), img.height(), dest_image).unwrap(),
)
}
DynamicImage::ImageRgb8(img) => {
let mut dest_image = vec![0u8; img.len()];
filter_2d_sep_rgb(
img.as_raw(),
&mut dest_image,
filter_image_size,
&x_axis_kernel,
&y_axis_kernel,
)
.unwrap();
DynamicImage::ImageRgb8(
RgbImage::from_raw(img.width(), img.height(), dest_image).unwrap(),
)
}
DynamicImage::ImageRgba8(img) => {
let mut dest_image = vec![0u8; img.len()];
filter_2d_sep_rgba(
img.as_raw(),
&mut dest_image,
filter_image_size,
&x_axis_kernel,
&y_axis_kernel,
)
.unwrap();
DynamicImage::ImageRgba8(
RgbaImage::from_raw(img.width(), img.height(), dest_image).unwrap(),
)
}
DynamicImage::ImageLuma16(img) => {
let mut dest_image = vec![0u16; img.len()];
filter_2d_sep_plane_u16(
img.as_raw(),
&mut dest_image,
filter_image_size,
&x_axis_kernel,
&y_axis_kernel,
)
.unwrap();
DynamicImage::ImageLuma16(
Gray16Image::from_raw(img.width(), img.height(), dest_image).unwrap(),
)
}
DynamicImage::ImageLumaA16(img) => {
let mut dest_image = vec![0u16; img.len()];
filter_2d_sep_la_u16(
img.as_raw(),
&mut dest_image,
filter_image_size,
&x_axis_kernel,
&y_axis_kernel,
)
.unwrap();
DynamicImage::ImageLumaA16(
GrayAlpha16Image::from_raw(img.width(), img.height(), dest_image).unwrap(),
)
}
DynamicImage::ImageRgb16(img) => {
let mut dest_image = vec![0u16; img.len()];
filter_2d_sep_rgb_u16(
img.as_raw(),
&mut dest_image,
filter_image_size,
&x_axis_kernel,
&y_axis_kernel,
)
.unwrap();
DynamicImage::ImageRgb16(
Rgb16Image::from_raw(img.width(), img.height(), dest_image).unwrap(),
)
}
DynamicImage::ImageRgba16(img) => {
let mut dest_image = vec![0u16; img.len()];
filter_2d_sep_rgba_u16(
img.as_raw(),
&mut dest_image,
filter_image_size,
&x_axis_kernel,
&y_axis_kernel,
)
.unwrap();
DynamicImage::ImageRgba16(
Rgba16Image::from_raw(img.width(), img.height(), dest_image).unwrap(),
)
}
DynamicImage::ImageRgb32F(img) => {
let mut dest_image = vec![0f32; img.len()];
filter_2d_sep_rgb_f32(
img.as_raw(),
&mut dest_image,
filter_image_size,
&x_axis_kernel,
&y_axis_kernel,
)
.unwrap();
DynamicImage::ImageRgb32F(
Rgb32FImage::from_raw(img.width(), img.height(), dest_image).unwrap(),
)
}
DynamicImage::ImageRgba32F(img) => {
let mut dest_image = vec![0f32; img.len()];
filter_2d_sep_rgba_f32(
img.as_raw(),
&mut dest_image,
filter_image_size,
&x_axis_kernel,
&y_axis_kernel,
)
.unwrap();
DynamicImage::ImageRgba32F(
Rgba32FImage::from_raw(img.width(), img.height(), dest_image).unwrap(),
)
}
};
// Must succeed.
let _ = target.set_color_space(image.color_space());
target
}
fn gaussian_blur_indirect(
image: &I,
parameters: GaussianBlurParameters,
) -> ImageBuffer::Subpixel>>
where
I::Pixel: 'static,
{
match I::Pixel::CHANNEL_COUNT {
1 => gaussian_blur_indirect_impl::(image, parameters),
2 => gaussian_blur_indirect_impl::(image, parameters),
3 => gaussian_blur_indirect_impl::(image, parameters),
4 => gaussian_blur_indirect_impl::(image, parameters),
_ => unimplemented!(),
}
}
fn gaussian_blur_indirect_impl(
image: &I,
parameters: GaussianBlurParameters,
) -> ImageBuffer::Subpixel>>
where
I::Pixel: 'static,
{
let mut transient = vec![0f32; image.width() as usize * image.height() as usize * CN];
for (pixel, dst) in image.pixels().zip(transient.chunks_exact_mut(CN)) {
let px = pixel.2.channels();
match CN {
1 => {
dst[0] = NumCast::from(px[0]).unwrap();
}
2 => {
dst[0] = NumCast::from(px[0]).unwrap();
dst[1] = NumCast::from(px[1]).unwrap();
}
3 => {
dst[0] = NumCast::from(px[0]).unwrap();
dst[1] = NumCast::from(px[1]).unwrap();
dst[2] = NumCast::from(px[2]).unwrap();
}
4 => {
dst[0] = NumCast::from(px[0]).unwrap();
dst[1] = NumCast::from(px[1]).unwrap();
dst[2] = NumCast::from(px[2]).unwrap();
dst[3] = NumCast::from(px[3]).unwrap();
}
_ => unreachable!(),
}
}
let mut transient_dst = vec![0.; image.width() as usize * image.height() as usize * CN];
let x_axis_kernel = get_gaussian_kernel_1d(
parameters.x_axis_kernel_size as usize,
parameters.x_axis_sigma,
);
let y_axis_kernel = get_gaussian_kernel_1d(
parameters.y_axis_kernel_size as usize,
parameters.y_axis_sigma,
);
let filter_image_size = FilterImageSize {
width: image.width() as usize,
height: image.height() as usize,
};
match CN {
1 => {
filter_2d_sep_plane_f32(
&transient,
&mut transient_dst,
filter_image_size,
&x_axis_kernel,
&y_axis_kernel,
)
.unwrap();
}
2 => {
filter_2d_sep_la_f32(
&transient,
&mut transient_dst,
filter_image_size,
&x_axis_kernel,
&y_axis_kernel,
)
.unwrap();
}
3 => {
filter_2d_sep_rgb_f32(
&transient,
&mut transient_dst,
filter_image_size,
&x_axis_kernel,
&y_axis_kernel,
)
.unwrap();
}
4 => {
filter_2d_sep_rgba_f32(
&transient,
&mut transient_dst,
filter_image_size,
&x_axis_kernel,
&y_axis_kernel,
)
.unwrap();
}
_ => unreachable!(),
}
let mut out = image.buffer_like();
for (dst, src) in out.pixels_mut().zip(transient_dst.chunks_exact_mut(CN)) {
match CN {
1 => {
let v0 = NumCast::from(FloatNearest(src[0])).unwrap();
#[allow(deprecated)]
let t = Pixel::from_channels(v0, v0, v0, v0);
*dst = t;
}
2 => {
let v0 = NumCast::from(FloatNearest(src[0])).unwrap();
let v1 = NumCast::from(FloatNearest(src[1])).unwrap();
#[allow(deprecated)]
let t = Pixel::from_channels(v0, v1, v0, v0);
*dst = t;
}
3 => {
let v0 = NumCast::from(FloatNearest(src[0])).unwrap();
let v1 = NumCast::from(FloatNearest(src[1])).unwrap();
let v2 = NumCast::from(FloatNearest(src[2])).unwrap();
#[allow(deprecated)]
let t = Pixel::from_channels(v0, v1, v2, v0);
*dst = t;
}
4 => {
let v0 = NumCast::from(FloatNearest(src[0])).unwrap();
let v1 = NumCast::from(FloatNearest(src[1])).unwrap();
let v2 = NumCast::from(FloatNearest(src[2])).unwrap();
let v3 = NumCast::from(FloatNearest(src[3])).unwrap();
#[allow(deprecated)]
let t = Pixel::from_channels(v0, v1, v2, v3);
*dst = t;
}
_ => unreachable!(),
}
}
out
}
/// Performs an unsharpen mask on the supplied image.
///
/// # Arguments:
///
/// * `sigma` - is the amount to blur the image by.
/// * `threshold` - is the threshold for minimal brightness change that will be sharpened.
///
/// This method typically assumes that the input is scene-linear light.
/// If it is not, color distortion may occur.
///
/// See [Digital unsharp masking](https://en.wikipedia.org/wiki/Unsharp_masking#Digital_unsharp_masking) for more information.
pub fn unsharpen(image: &I, sigma: f32, threshold: i32) -> ImageBuffer>
where
I: GenericImageView,
P: Pixel + 'static,
S: Primitive + 'static,
{
let mut tmp = blur_advanced(image, GaussianBlurParameters::new_from_sigma(sigma));
let max = S::DEFAULT_MAX_VALUE;
let max: i32 = NumCast::from(max).unwrap();
let (width, height) = image.dimensions();
for y in 0..height {
for x in 0..width {
let a = image.get_pixel(x, y);
let b = tmp.get_pixel_mut(x, y);
let p = a.map2(b, |c, d| {
let ic: i32 = NumCast::from(c).unwrap();
let id: i32 = NumCast::from(d).unwrap();
let diff = ic - id;
if diff.abs() > threshold {
let e = clamp(ic + diff, 0, max); // FIXME what does this do for f32? clamp 0-1 integers??
NumCast::from(e).unwrap()
} else {
c
}
});
*b = p;
}
}
tmp
}
#[cfg(test)]
mod tests {
use super::{resize, sample_bilinear, sample_nearest, FilterType};
use crate::{GenericImageView, ImageBuffer, RgbImage};
#[cfg(feature = "benchmarks")]
use test;
#[bench]
#[cfg(all(feature = "benchmarks", feature = "png"))]
fn bench_resize(b: &mut test::Bencher) {
use std::path::Path;
let img = crate::open(Path::new("./examples/fractal.png")).unwrap();
b.iter(|| {
test::black_box(resize(&img, 200, 200, FilterType::Nearest));
});
b.bytes = 800 * 800 * 3 + 200 * 200 * 3;
}
#[test]
#[cfg(feature = "png")]
fn test_resize_same_size() {
use std::path::Path;
let img = crate::open(Path::new("./examples/fractal.png")).unwrap();
let resize = img.resize(img.width(), img.height(), FilterType::Triangle);
assert!(img.pixels().eq(resize.pixels()));
}
#[test]
#[cfg(feature = "png")]
fn test_sample_bilinear() {
use std::path::Path;
let img = crate::open(Path::new("./examples/fractal.png")).unwrap();
assert!(sample_bilinear(&img, 0., 0.).is_some());
assert!(sample_bilinear(&img, 1., 0.).is_some());
assert!(sample_bilinear(&img, 0., 1.).is_some());
assert!(sample_bilinear(&img, 1., 1.).is_some());
assert!(sample_bilinear(&img, 0.5, 0.5).is_some());
assert!(sample_bilinear(&img, 1.2, 0.5).is_none());
assert!(sample_bilinear(&img, 0.5, 1.2).is_none());
assert!(sample_bilinear(&img, 1.2, 1.2).is_none());
assert!(sample_bilinear(&img, -0.1, 0.2).is_none());
assert!(sample_bilinear(&img, 0.2, -0.1).is_none());
assert!(sample_bilinear(&img, -0.1, -0.1).is_none());
}
#[test]
#[cfg(feature = "png")]
fn test_sample_nearest() {
use std::path::Path;
let img = crate::open(Path::new("./examples/fractal.png")).unwrap();
assert!(sample_nearest(&img, 0., 0.).is_some());
assert!(sample_nearest(&img, 1., 0.).is_some());
assert!(sample_nearest(&img, 0., 1.).is_some());
assert!(sample_nearest(&img, 1., 1.).is_some());
assert!(sample_nearest(&img, 0.5, 0.5).is_some());
assert!(sample_nearest(&img, 1.2, 0.5).is_none());
assert!(sample_nearest(&img, 0.5, 1.2).is_none());
assert!(sample_nearest(&img, 1.2, 1.2).is_none());
assert!(sample_nearest(&img, -0.1, 0.2).is_none());
assert!(sample_nearest(&img, 0.2, -0.1).is_none());
assert!(sample_nearest(&img, -0.1, -0.1).is_none());
}
#[test]
fn test_sample_bilinear_correctness() {
use crate::Rgba;
let img = ImageBuffer::from_fn(2, 2, |x, y| match (x, y) {
(0, 0) => Rgba([255, 0, 0, 0]),
(0, 1) => Rgba([0, 255, 0, 0]),
(1, 0) => Rgba([0, 0, 255, 0]),
(1, 1) => Rgba([0, 0, 0, 255]),
_ => panic!(),
});
assert_eq!(sample_bilinear(&img, 0.5, 0.5), Some(Rgba([64; 4])));
assert_eq!(sample_bilinear(&img, 0.0, 0.0), Some(Rgba([255, 0, 0, 0])));
assert_eq!(sample_bilinear(&img, 0.0, 1.0), Some(Rgba([0, 255, 0, 0])));
assert_eq!(sample_bilinear(&img, 1.0, 0.0), Some(Rgba([0, 0, 255, 0])));
assert_eq!(sample_bilinear(&img, 1.0, 1.0), Some(Rgba([0, 0, 0, 255])));
assert_eq!(
sample_bilinear(&img, 0.5, 0.0),
Some(Rgba([128, 0, 128, 0]))
);
assert_eq!(
sample_bilinear(&img, 0.0, 0.5),
Some(Rgba([128, 128, 0, 0]))
);
assert_eq!(
sample_bilinear(&img, 0.5, 1.0),
Some(Rgba([0, 128, 0, 128]))
);
assert_eq!(
sample_bilinear(&img, 1.0, 0.5),
Some(Rgba([0, 0, 128, 128]))
);
}
#[bench]
#[cfg(feature = "benchmarks")]
fn bench_sample_bilinear(b: &mut test::Bencher) {
use crate::Rgba;
let img = ImageBuffer::from_fn(2, 2, |x, y| match (x, y) {
(0, 0) => Rgba([255, 0, 0, 0]),
(0, 1) => Rgba([0, 255, 0, 0]),
(1, 0) => Rgba([0, 0, 255, 0]),
(1, 1) => Rgba([0, 0, 0, 255]),
_ => panic!(),
});
b.iter(|| {
sample_bilinear(&img, test::black_box(0.5), test::black_box(0.5));
});
}
#[test]
fn test_sample_nearest_correctness() {
use crate::Rgba;
let img = ImageBuffer::from_fn(2, 2, |x, y| match (x, y) {
(0, 0) => Rgba([255, 0, 0, 0]),
(0, 1) => Rgba([0, 255, 0, 0]),
(1, 0) => Rgba([0, 0, 255, 0]),
(1, 1) => Rgba([0, 0, 0, 255]),
_ => panic!(),
});
assert_eq!(sample_nearest(&img, 0.0, 0.0), Some(Rgba([255, 0, 0, 0])));
assert_eq!(sample_nearest(&img, 0.0, 1.0), Some(Rgba([0, 255, 0, 0])));
assert_eq!(sample_nearest(&img, 1.0, 0.0), Some(Rgba([0, 0, 255, 0])));
assert_eq!(sample_nearest(&img, 1.0, 1.0), Some(Rgba([0, 0, 0, 255])));
assert_eq!(sample_nearest(&img, 0.5, 0.5), Some(Rgba([0, 0, 0, 255])));
assert_eq!(sample_nearest(&img, 0.5, 0.0), Some(Rgba([0, 0, 255, 0])));
assert_eq!(sample_nearest(&img, 0.0, 0.5), Some(Rgba([0, 255, 0, 0])));
assert_eq!(sample_nearest(&img, 0.5, 1.0), Some(Rgba([0, 0, 0, 255])));
assert_eq!(sample_nearest(&img, 1.0, 0.5), Some(Rgba([0, 0, 0, 255])));
}
#[bench]
#[cfg(all(feature = "benchmarks", feature = "tiff"))]
fn bench_resize_same_size(b: &mut test::Bencher) {
let path = concat!(
env!("CARGO_MANIFEST_DIR"),
"/tests/images/tiff/testsuite/mandrill.tiff"
);
let image = crate::open(path).unwrap();
b.iter(|| {
test::black_box(image.resize(image.width(), image.height(), FilterType::CatmullRom));
});
b.bytes = u64::from(image.width() * image.height() * 3);
}
#[test]
fn test_issue_186() {
let img: RgbImage = ImageBuffer::new(100, 100);
let _ = resize(&img, 50, 50, FilterType::Lanczos3);
}
#[bench]
#[cfg(all(feature = "benchmarks", feature = "tiff"))]
fn bench_thumbnail(b: &mut test::Bencher) {
let path = concat!(
env!("CARGO_MANIFEST_DIR"),
"/tests/images/tiff/testsuite/mandrill.tiff"
);
let image = crate::open(path).unwrap();
b.iter(|| {
test::black_box(image.thumbnail(256, 256));
});
b.bytes = 512 * 512 * 4 + 256 * 256 * 4;
}
#[bench]
#[cfg(all(feature = "benchmarks", feature = "tiff"))]
fn bench_thumbnail_upsize(b: &mut test::Bencher) {
let path = concat!(
env!("CARGO_MANIFEST_DIR"),
"/tests/images/tiff/testsuite/mandrill.tiff"
);
let image = crate::open(path).unwrap().thumbnail(256, 256);
b.iter(|| {
test::black_box(image.thumbnail(512, 512));
});
b.bytes = 512 * 512 * 4 + 256 * 256 * 4;
}
#[bench]
#[cfg(all(feature = "benchmarks", feature = "tiff"))]
fn bench_thumbnail_upsize_irregular(b: &mut test::Bencher) {
let path = concat!(
env!("CARGO_MANIFEST_DIR"),
"/tests/images/tiff/testsuite/mandrill.tiff"
);
let image = crate::open(path).unwrap().thumbnail(193, 193);
b.iter(|| {
test::black_box(image.thumbnail(256, 256));
});
b.bytes = 193 * 193 * 4 + 256 * 256 * 4;
}
#[test]
#[cfg(feature = "png")]
fn resize_transparent_image() {
use super::FilterType::{CatmullRom, Gaussian, Lanczos3, Nearest, Triangle};
use crate::imageops::crop_imm;
use crate::RgbaImage;
fn assert_resize(image: &RgbaImage, filter: FilterType) {
let resized = resize(image, 16, 16, filter);
let cropped = crop_imm(&resized, 5, 5, 6, 6).to_image();
for pixel in cropped.pixels() {
let alpha = pixel.0[3];
assert!(
alpha != 254 && alpha != 253,
"alpha value: {alpha}, {filter:?}"
);
}
}
let path = concat!(
env!("CARGO_MANIFEST_DIR"),
"/tests/images/png/transparency/tp1n3p08.png"
);
let img = crate::open(path).unwrap();
let rgba8 = img.as_rgba8().unwrap();
let filters = &[Nearest, Triangle, CatmullRom, Gaussian, Lanczos3];
for filter in filters {
assert_resize(rgba8, *filter);
}
}
#[test]
fn bug_1600() {
let image = crate::RgbaImage::from_raw(629, 627, vec![255; 629 * 627 * 4]).unwrap();
let result = resize(&image, 22, 22, FilterType::Lanczos3);
assert!(result.into_raw().into_iter().any(|c| c != 0));
}
#[test]
fn issue_2340() {
let empty = crate::GrayImage::from_raw(1 << 31, 0, vec![]).unwrap();
// Really we're checking that no overflow / outsized allocation happens here.
let result = resize(&empty, 1, 1, FilterType::Lanczos3);
assert!(result.into_raw().into_iter().all(|c| c == 0));
// With the previous strategy before the regression this would allocate 1TB of memory for a
// temporary during the sampling evaluation.
let result = resize(&empty, 256, 256, FilterType::Lanczos3);
assert!(result.into_raw().into_iter().all(|c| c == 0));
}
#[test]
fn issue_2340_refl() {
// Tests the swapped coordinate version of `issue_2340`.
let empty = crate::GrayImage::from_raw(0, 1 << 31, vec![]).unwrap();
let result = resize(&empty, 1, 1, FilterType::Lanczos3);
assert!(result.into_raw().into_iter().all(|c| c == 0));
let result = resize(&empty, 256, 256, FilterType::Lanczos3);
assert!(result.into_raw().into_iter().all(|c| c == 0));
}
}
