/src/image/src/imageops/sample.rs

Source
//! Functions and filters for the sampling of pixels.
//!
//! Some of these functions assume structure of the `Pixel` type beyond the trait interface.
//! Generally: methods operating on individual channels have a limit of 4 channels.

// See http://cs.brown.edu/courses/cs123/lectures/08_Image_Processing_IV.pdf
// for some of the theory behind image scaling and convolution

use num_traits::{NumCast, ToPrimitive, Zero};
use std::f32;
use std::ops::Mul;

use crate::imageops::filter_1d::{
    filter_2d_sep_la, filter_2d_sep_la_f32, filter_2d_sep_la_u16, filter_2d_sep_plane,
    filter_2d_sep_plane_f32, filter_2d_sep_plane_u16, filter_2d_sep_rgb, filter_2d_sep_rgb_f32,
    filter_2d_sep_rgb_u16, filter_2d_sep_rgba, filter_2d_sep_rgba_f32, filter_2d_sep_rgba_u16,
    FilterImageSize,
};
use crate::images::buffer::{Gray16Image, GrayAlpha16Image, Rgb16Image, Rgba16Image};
use crate::traits::{Enlargeable, Pixel, Primitive};
use crate::utils::{clamp, is_integer};
use crate::{
    DynamicImage, GenericImage, GenericImageView, GrayAlphaImage, GrayImage, ImageBuffer,
    Rgb32FImage, RgbImage, Rgba32FImage, RgbaImage,
};

const MAX_CHANNEL: usize = 4;

/// Available Sampling Filters.
///
/// ## Examples
///
/// To test the different sampling filters on a real example, you can find two
/// examples called
/// [`scaledown`](https://github.com/image-rs/image/tree/main/examples/scaledown)
/// and
/// [`scaleup`](https://github.com/image-rs/image/tree/main/examples/scaleup)
/// in the `examples` directory of the crate source code.
///
/// Here is a 3.58 MiB
/// [test image](https://github.com/image-rs/image/blob/main/examples/scaledown/test.jpg)
/// that has been scaled down to 300x225 px:
///
/// <!-- NOTE: To test new test images locally, replace the GitHub path with `../../../docs/` -->
/// <div style="display: flex; flex-wrap: wrap; align-items: flex-start;">
///   <div style="margin: 0 8px 8px 0;">
///     <img src="https://raw.githubusercontent.com/image-rs/image/main/examples/scaledown/scaledown-test-near.png" title="Nearest"><br>
///     Nearest Neighbor
///   </div>
///   <div style="margin: 0 8px 8px 0;">
///     <img src="https://raw.githubusercontent.com/image-rs/image/main/examples/scaledown/scaledown-test-tri.png" title="Triangle"><br>
///     Linear: Triangle
///   </div>
///   <div style="margin: 0 8px 8px 0;">
///     <img src="https://raw.githubusercontent.com/image-rs/image/main/examples/scaledown/scaledown-test-cmr.png" title="CatmullRom"><br>
///     Cubic: Catmull-Rom
///   </div>
///   <div style="margin: 0 8px 8px 0;">
///     <img src="https://raw.githubusercontent.com/image-rs/image/main/examples/scaledown/scaledown-test-gauss.png" title="Gaussian"><br>
///     Gaussian
///   </div>
///   <div style="margin: 0 8px 8px 0;">
///     <img src="https://raw.githubusercontent.com/image-rs/image/main/examples/scaledown/scaledown-test-lcz2.png" title="Lanczos3"><br>
///     Lanczos with window 3
///   </div>
/// </div>
///
/// ## Speed
///
/// Time required to create each of the examples above, tested on an Intel
/// i7-4770 CPU with Rust 1.37 in release mode:
///
/// <table style="width: auto;">
///   <tr>
///     <th>Nearest</th>
///     <td>31 ms</td>
///   </tr>
///   <tr>
///     <th>Triangle</th>
///     <td>414 ms</td>
///   </tr>
///   <tr>
///     <th>CatmullRom</th>
///     <td>817 ms</td>
///   </tr>
///   <tr>
///     <th>Gaussian</th>
///     <td>1180 ms</td>
///   </tr>
///   <tr>
///     <th>Lanczos3</th>
///     <td>1170 ms</td>
///   </tr>
/// </table>
#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash)]
#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
pub enum FilterType {
    /// Nearest Neighbor
    Nearest,

    /// Linear Filter
    Triangle,

    /// Cubic Filter
    CatmullRom,

    /// Gaussian Filter
    Gaussian,

    /// Lanczos with window 3
    Lanczos3,
}

/// A Representation of a separable filter.
pub(crate) struct Filter<'a> {
    /// The filter's filter function.
    pub(crate) kernel: Box<dyn Fn(f32) -> f32 + 'a>,

    /// The window on which this filter operates.
    pub(crate) support: f32,
}

struct FloatNearest(f32);

// to_i64, to_u64, and to_f64 implicitly affect all other lower conversions.
// Note that to_f64 by default calls to_i64 and thus needs to be overridden.
impl ToPrimitive for FloatNearest {
    // to_{i,u}64 is required, to_{i,u}{8,16} are useful.
    // If a usecase for full 32 bits is found its trivial to add
    fn to_i8(&self) -> Option<i8> {
        self.0.round().to_i8()
    }
    fn to_i16(&self) -> Option<i16> {
        self.0.round().to_i16()
    }
    fn to_i64(&self) -> Option<i64> {
        self.0.round().to_i64()
    }
    fn to_u8(&self) -> Option<u8> {
        self.0.round().to_u8()
    }
    fn to_u16(&self) -> Option<u16> {
        self.0.round().to_u16()
    }
    fn to_u64(&self) -> Option<u64> {
        self.0.round().to_u64()
    }
    fn to_f64(&self) -> Option<f64> {
        self.0.to_f64()
    }
}

// sinc function: the ideal sampling filter.
fn sinc(t: f32) -> f32 {
    let a = t * f32::consts::PI;

    if t == 0.0 {
        1.0
    } else {
        a.sin() / a
    }
}

// lanczos kernel function. A windowed sinc function.
fn lanczos(x: f32, t: f32) -> f32 {
    if x.abs() < t {
        sinc(x) * sinc(x / t)
    } else {
        0.0
    }
}

// Calculate a splice based on the b and c parameters.
// from authors Mitchell and Netravali.
fn bc_cubic_spline(x: f32, b: f32, c: f32) -> f32 {
    let a = x.abs();

    let k = if a < 1.0 {
        (12.0 - 9.0 * b - 6.0 * c) * a.powi(3)
            + (-18.0 + 12.0 * b + 6.0 * c) * a.powi(2)
            + (6.0 - 2.0 * b)
    } else if a < 2.0 {
        (-b - 6.0 * c) * a.powi(3)
            + (6.0 * b + 30.0 * c) * a.powi(2)
            + (-12.0 * b - 48.0 * c) * a
            + (8.0 * b + 24.0 * c)
    } else {
        0.0
    };

    k / 6.0
}

/// The Gaussian Function.
/// ```r``` is the standard deviation.
pub(crate) fn gaussian(x: f32, r: f32) -> f32 {
    ((2.0 * f32::consts::PI).sqrt() * r).recip() * (-x.powi(2) / (2.0 * r.powi(2))).exp()
}

/// Calculate the lanczos kernel with a window of 3
pub(crate) fn lanczos3_kernel(x: f32) -> f32 {
    lanczos(x, 3.0)
}

/// Calculate the gaussian function with a
/// standard deviation of 0.5
pub(crate) fn gaussian_kernel(x: f32) -> f32 {
    gaussian(x, 0.5)
}

/// Calculate the Catmull-Rom cubic spline.
/// Also known as a form of `BiCubic` sampling in two dimensions.
pub(crate) fn catmullrom_kernel(x: f32) -> f32 {
    bc_cubic_spline(x, 0.0, 0.5)
}

/// Calculate the triangle function.
/// Also known as `BiLinear` sampling in two dimensions.
pub(crate) fn triangle_kernel(x: f32) -> f32 {
    if x.abs() < 1.0 {
        1.0 - x.abs()
    } else {
        0.0
    }
}

/// Calculate the box kernel.
/// Only pixels inside the box should be considered, and those
/// contribute equally.  So this method simply returns 1.
pub(crate) fn box_kernel(_x: f32) -> f32 {
    1.0
}

// Sample the rows of the supplied image using the provided filter.
//
// The height of the image remains unchanged.
// ```new_width``` is the desired width of the new image
// ```filter``` is the filter to use for sampling.
// ```image``` is not necessarily Rgba and the order of channels is passed through.
//
// Note: if an empty image is passed in, panics unless the image is truly empty.
//
// Note: this function processes pixels for the standard pixel types with up to 4 channels.
fn horizontal_sample<P, S>(
    image: &Rgba32FImage,
    new_width: u32,
    filter: &mut Filter,
) -> ImageBuffer<P, Vec<S>>
where
    P: Pixel<Subpixel = S>,
    S: Primitive,
{
    let (width, height) = image.dimensions();
    // This is protection against a memory usage similar to #2340. See `vertical_sample`.
    assert!(
        // Checks the implication: (width == 0) -> (height == 0)
        width != 0 || height == 0,
        "Unexpected prior allocation size. This case should have been handled by the caller"
    );

    let mut out = ImageBuffer::new(new_width, height);
    out.copy_color_space_from(image);
    let mut ws = Vec::new();

    let max: f32 = NumCast::from(S::DEFAULT_MAX_VALUE).unwrap();
    let min: f32 = NumCast::from(S::DEFAULT_MIN_VALUE).unwrap();
    let ratio = width as f32 / new_width as f32;
    let sratio = if ratio < 1.0 { 1.0 } else { ratio };
    let src_support = filter.support * sratio;

    let mut pix_temp = <P as Pixel>::broadcast(S::DEFAULT_MAX_VALUE);

    for outx in 0..new_width {
        // Find the point in the input image corresponding to the centre
        // of the current pixel in the output image.
        let inputx = (outx as f32 + 0.5) * ratio;

        // Left and right are slice bounds for the input pixels relevant
        // to the output pixel we are calculating.  Pixel x is relevant
        // if and only if (x >= left) && (x < right).

        // Invariant: 0 <= left < right <= width

        let left = (inputx - src_support).floor() as i64;
        let left = clamp(left, 0, <i64 as From<_>>::from(width) - 1) as u32;

        let right = (inputx + src_support).ceil() as i64;
        let right = clamp(
            right,
            <i64 as From<_>>::from(left) + 1,
            <i64 as From<_>>::from(width),
        ) as u32;

        // Go back to left boundary of pixel, to properly compare with i
        // below, as the kernel treats the centre of a pixel as 0.
        let inputx = inputx - 0.5;

        ws.clear();
        let mut sum = 0.0;
        for i in left..right {
            let w = (filter.kernel)((i as f32 - inputx) / sratio);
            ws.push(w);
            sum += w;
        }
        for w in ws.iter_mut() {
            *w /= sum;
        }

        for y in 0..height {
            let mut t = [0.0; MAX_CHANNEL];

            for (i, w) in ws.iter().enumerate() {
                let p = image.get_pixel(left + i as u32, y);

                for (t, &c) in t.iter_mut().zip(p.channels()) {
                    *t += c * w;
                }
            }

            for (&tc, pc) in t.iter().zip(pix_temp.channels_mut()) {
                *pc = NumCast::from(FloatNearest(clamp(tc, min, max))).unwrap();
            }

            out.put_pixel(outx, y, pix_temp);
        }
    }

    out
}

/// Linearly sample from an image using coordinates in [0, 1].
pub fn sample_bilinear<P: Pixel>(
    img: &impl GenericImageView<Pixel = P>,
    u: f32,
    v: f32,
) -> Option<P> {
    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<P: Pixel>(
    img: &impl GenericImageView<Pixel = P>,
    u: f32,
    v: f32,
) -> Option<P> {
    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<P: Pixel>(
    img: &impl GenericImageView<Pixel = P>,
    x: f32,
    y: f32,
) -> Option<P> {
    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<P: Pixel>(
    img: &impl GenericImageView<Pixel = P>,
    x: f32,
    y: f32,
) -> Option<P> {
    // assumption needed for correctness of pixel creation
    assert!(P::CHANNEL_COUNT as usize <= MAX_CHANNEL);

    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.; MAX_CHANNEL]; MAX_CHANNEL];

    // do not use Array::map, as it can be slow with high stack usage,
    // for [[f32; MAX_CHANNEL4]; MAX_CHANNEL4].

    // 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 = <P::Subpixel as NumCast>::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<I, P, S>(image: &I, new_height: u32, filter: &mut Filter) -> Rgba32FImage
where
    I: GenericImageView<Pixel = P>,
    P: Pixel<Subpixel = S>,
    S: Primitive,
{
    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, <i64 as From<_>>::from(height) - 1) as u32;

        let right = (inputy + src_support).ceil() as i64;
        let right = clamp(
            right,
            <i64 as From<_>>::from(left) + 1,
            <i64 as From<_>>::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 pix = crate::Rgba([1.0; 4]);

            for (i, w) in ws.iter().enumerate() {
                let p = image.get_pixel(x, left + i as u32);

                for (tc, &c) in pix.channels_mut().iter_mut().zip(p.channels()) {
                    *tc += <f32 as NumCast>::from(c).unwrap() * w;
                }
            }

            out.put_pixel(x, outy, pix);
        }
    }

    out
}

/// Local struct for keeping track of pixel sums for fast thumbnail averaging
struct ThumbnailSum<S: Primitive + Enlargeable>([S::Larger; MAX_CHANNEL]);

impl<S: Primitive + Enlargeable> ThumbnailSum<S> {
    fn zeroed() -> Self {
        ThumbnailSum([S::Larger::zero(); MAX_CHANNEL])
    }

    fn sample_val(val: S) -> S::Larger {
        <S::Larger as NumCast>::from(val).unwrap()
    }

    fn add_pixel<P: Pixel<Subpixel = S>>(&mut self, pixel: P) {
        for (s, &c) in self.0.iter_mut().zip(pixel.channels()) {
            *s += Self::sample_val(c);
        }
    }
}

/// 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<I, P, S>(image: &I, new_width: u32, new_height: u32) -> ImageBuffer<P, Vec<S>>
where
    I: GenericImageView<Pixel = P>,
    P: Pixel<Subpixel = S>,
    S: Primitive + Enlargeable,
{
    // Maximum support channels for `ThumbnailSum`.
    assert!(P::CHANNEL_COUNT as usize <= MAX_CHANNEL);

    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,
                )
            };

            out.put_pixel(outx, outy, avg);
        }
    }

    out
}

/// Get a pixel for a thumbnail where the input window encloses at least a full pixel.
fn thumbnail_sample_block<I, P, S>(image: &I, left: u32, right: u32, bottom: u32, top: u32) -> P
where
    I: GenericImageView<Pixel = P>,
    P: Pixel<Subpixel = S>,
    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 = <S::Larger as NumCast>::from((right - left) * (top - bottom)).unwrap();

    // For integer types division truncates, so we need to add n/2 to round to
    // the nearest integer. Floating point types do not need this.
    let round = if is_integer::<S::Larger>() {
        <S::Larger as NumCast>::from(n / NumCast::from(2).unwrap()).unwrap()
    } else {
        S::Larger::zero()
    };

    let rounded_avg = sum.0.map(|v| S::clamp_from((v + round) / n));
    let used_channels: usize = (<P as Pixel>::CHANNEL_COUNT).min(4u8).into();
    *<P as Pixel>::from_slice(&rounded_avg[..used_channels])
}

/// Get a thumbnail pixel where the input window encloses at least a vertical pixel.
fn thumbnail_sample_fraction_horizontal<I, P, S>(
    image: &I,
    left: u32,
    fraction_horizontal: f32,
    bottom: u32,
    top: u32,
) -> P
where
    I: GenericImageView<Pixel = P>,
    P: Pixel<Subpixel = S>,
    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| {
        <S as NumCast>::from(
            fact_left * leftv.to_f32().unwrap() + fact_right * rightv.to_f32().unwrap(),
        )
        .expect("Average sample value should fit into sample type")
    };

    let left = sum_left.0;
    let right = sum_right.0;

    let mixed_sum = [
        mix_left_and_right(left[0], right[0]),
        mix_left_and_right(left[1], right[1]),
        mix_left_and_right(left[2], right[2]),
        mix_left_and_right(left[3], right[3]),
    ];

    let used_channels: usize = (<P as Pixel>::CHANNEL_COUNT).min(4u8).into();
    *<P as Pixel>::from_slice(&mixed_sum[..used_channels])
}

/// Get a thumbnail pixel where the input window encloses at least a horizontal pixel.
fn thumbnail_sample_fraction_vertical<I, P, S>(
    image: &I,
    left: u32,
    right: u32,
    bottom: u32,
    fraction_vertical: f32,
) -> P
where
    I: GenericImageView<Pixel = P>,
    P: Pixel<Subpixel = S>,
    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| {
        <S as NumCast>::from(fact_bot * botv.to_f32().unwrap() + fact_top * topv.to_f32().unwrap())
            .expect("Average sample value should fit into sample type")
    };

    let bot = sum_bot.0;
    let top = sum_top.0;

    let mixed_sum = [
        mix_bot_and_top(bot[0], top[0]),
        mix_bot_and_top(bot[1], top[1]),
        mix_bot_and_top(bot[2], top[2]),
        mix_bot_and_top(bot[3], top[3]),
    ];

    let used_channels: usize = (<P as Pixel>::CHANNEL_COUNT).min(4u8).into();
    *<P as Pixel>::from_slice(&mixed_sum[..used_channels])
}

/// Get a single pixel for a thumbnail where the input window does not enclose any full pixel.
fn thumbnail_sample_fraction_both<I, P, S>(
    image: &I,
    left: u32,
    fraction_vertical: f32,
    bottom: u32,
    fraction_horizontal: f32,
) -> P
where
    I: GenericImageView<Pixel = P>,
    P: Pixel<Subpixel = S>,
    S: Primitive + Enlargeable,
{
    let k_bl = image.get_pixel(left, bottom);
    let k_tl = image.get_pixel(left, bottom + 1);
    let k_br = image.get_pixel(left + 1, bottom);
    let k_tr = image.get_pixel(left + 1, bottom + 1);

    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| {
        <S as NumCast>::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")
    };

    // Make a copy of any pixel, we'll fully overwrite it.
    let mut sum = k_br;

    for (i, ch) in sum.channels_mut().iter_mut().enumerate() {
        *ch = mix(
            k_br.channels()[i],
            k_tr.channels()[i],
            k_bl.channels()[i],
            k_tl.channels()[i],
        );
    }

    sum
}

/// 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<I, P, S>(image: &I, kernel: &[f32; 9]) -> ImageBuffer<P, Vec<S>>
where
    I: GenericImageView<Pixel = P>,
    P: Pixel<Subpixel = S>,
    S: Primitive,
{
    // 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();

    let inverse_sum = match kernel.iter().sum() {
        0.0 => 1.0,
        sum => 1.0 / sum,
    };

    for y in 1..height - 1 {
        for x in 1..width - 1 {
            let mut t = [0.0; MAX_CHANNEL];

            // 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 x0 = x as isize + a;
                let y0 = y as isize + b;

                let p = image.get_pixel(x0 as u32, y0 as u32);

                for (tc, &c) in t.iter_mut().zip(p.channels()) {
                    *tc += <f32 as NumCast>::from(c).unwrap() * k;
                }
            }

            let channel_avg = t
                .map(|ti| ti * inverse_sum)
                .map(|ti| NumCast::from(clamp(ti, 0.0, max)).unwrap());

            let pix = *Pixel::from_slice(&channel_avg[..P::CHANNEL_COUNT as usize]);

            out.put_pixel(x, y, pix);
        }
    }

    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<I: GenericImageView>(
    image: &I,
    nwidth: u32,
    nheight: u32,
    filter: FilterType,
) -> ImageBuffer<I::Pixel, Vec<<I::Pixel as Pixel>::Subpixel>> {
    // 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
///
/// * `radius` - Blurring window radius. These parameter controls directly the window size.
///   We choose visually optimal sigma for the given radius. To control sigma
///   directly use [DynamicImage::blur_advanced] instead.
///
/// 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<I: GenericImageView>(
    image: &I,
    radius: f32,
) -> ImageBuffer<I::Pixel, Vec<<I::Pixel as Pixel>::Subpixel>> {
    gaussian_blur_indirect(image, GaussianBlurParameters::new_from_radius(radius))
}

/// 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<I: GenericImageView>(
    image: &I,
    parameters: GaussianBlurParameters,
) -> ImageBuffer<I::Pixel, Vec<<I::Pixel as Pixel>::Subpixel>> {
    gaussian_blur_indirect(image, parameters)
}

fn get_gaussian_kernel_1d(width: usize, sigma: f32) -> Vec<f32> {
    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
    ///
    /// Sigma controls how flat the sample is. A visually optimal sigma is around
    /// `window_size / 6`.
    /// When sigma is close to the window size, the sample becomes almost flat,
    /// behaving like a windowed average or a box blur.
    /// When sigma is close to 1, only center pixels are involved in windowing,
    /// so a smaller window is preferred in this case.
    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
    ///
    /// Sigma controls how flat the sample is. A visually optimal sigma is around
    /// `window_size / 6`.
    /// When sigma is close to the window size, the sample becomes almost flat,
    /// behaving like a windowed average or a box blur.
    /// When sigma is close to 1, only center pixels are involved in windowing,
    /// so a smaller window is preferred in this case.
    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_from_kernel_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_from_kernel_size(x_axis_kernel_size);
        let y_sigma = GaussianBlurParameters::sigma_from_kernel_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
    ///
    /// # Panics
    /// Panics if sigma is negative, NaN or infinity.
    pub fn new_from_sigma(sigma: f32) -> GaussianBlurParameters {
        assert!(
            sigma >= 0.0 && sigma.is_finite(),
            "Sigma must be non-negative and finite"
        );

        /// As sigma gets smaller, the blur kernel quickly approaches all zeros
        /// with the center being 1, which is an identity for 1D convolution.
        /// I.e. no blur. So we define a threshold for sigma below which we
        /// consider the blur to be an identity.
        ///
        /// Small sigma generally map to a 3x1 kernel. This kernel will have the
        /// values `[w, 1.0, w]` before normalization (ignoring shared factors),
        /// where `w = exp(-0.5 / pow(sigma, 2))`. If the sum of weights is
        /// rounded to 1, i.e. `1.0 + 2.0 * w == 1.0`, then w is small enough
        /// that the kernel is effectively an identity. So we pick this
        /// threshold as the largest sigma such that
        /// `1.0 + 2.0 * exp(-0.5 / pow(sigma, 2)) == 1.0` when rounded to f32
        /// precision.
        const IDENTITY_THRESHOLD: f32 = 0.16986436;
        if sigma < IDENTITY_THRESHOLD {
            // Any (normalized) kernel of size 1 is the identity, so sigma
            // doesn't matter. However, we pick sigma=1 to avoid  potential
            // issues with NaN, infinities, and subnormals.
            return GaussianBlurParameters {
                x_axis_kernel_size: 1,
                x_axis_sigma: 1.0,
                y_axis_kernel_size: 1,
                y_axis_sigma: 1.0,
            };
        }

        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.is_multiple_of(2) {
            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_from_kernel_size(kernel_size: f32) -> f32 {
        let safe_kernel_size = if kernel_size <= 1. { 0.8 } else { kernel_size };
        // This formula finds a "optimal perceptual sigma" for a given kernel
        // size. It is also used by other libraries, such as OpenCV:
        // https://docs.opencv.org/4.x/d4/d86/group__imgproc__filter.html#gac05a120c1ae92a6060dd0db190a61afa
        0.3 * ((safe_kernel_size - 1.) * 0.5 - 1.) + 0.8
    }

    fn kernel_size_from_sigma(sigma: f32) -> u32 {
        // This uses the inverse of the formula in `sigma_from_kernel_size` to
        // find the kernel size for a given sigma. This isn't the only valid
        // choice. For example, OpenCV uses 6σ+1 as the kernel size. Both work.
        // Note that a kernel size of 6.666σ convers 99.91% of the Gaussian
        // distribution, resulting in a high-quality blur without ringing
        // artifacts.
        //
        // Some more details:
        // - The returned kernel size is at least 3. This is important for small
        //   sigmas, since a kernel size of 1 would no no blur at all.
        // - The `| 1` ensure that the returned kernel size is odd, which is
        //   required. In effect, even numbers are rounded up to the next odd
        //   number, which is the same as the behavior of `round_to_nearest_odd`.
        (6.66666 * sigma - 2.3333333).max(3.) as u32 | 1
    }
}

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<I: GenericImageView>(
    image: &I,
    parameters: GaussianBlurParameters,
) -> ImageBuffer<I::Pixel, Vec<<I::Pixel as Pixel>::Subpixel>> {
    match I::Pixel::CHANNEL_COUNT {
        1 => gaussian_blur_indirect_impl::<I, 1>(image, parameters),
        2 => gaussian_blur_indirect_impl::<I, 2>(image, parameters),
        3 => gaussian_blur_indirect_impl::<I, 3>(image, parameters),
        4 => gaussian_blur_indirect_impl::<I, 4>(image, parameters),
        _ => unimplemented!(),
    }
}

fn gaussian_blur_indirect_impl<I: GenericImageView, const CN: usize>(
    image: &I,
    parameters: GaussianBlurParameters,
) -> ImageBuffer<I::Pixel, Vec<<I::Pixel as Pixel>::Subpixel>> {
    let mut transient = vec![0f32; image.width() as usize * image.height() as usize * CN];
    let transient_chunks = transient.as_chunks_mut::<CN>().0.iter_mut();
    for (pixel, dst) in image.pixels().zip(transient_chunks) {
        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();
    let transient_dst_chunks = transient_dst.as_chunks_mut::<CN>().0.iter_mut();
    for (dst, src) in out.pixels_mut().zip(transient_dst_chunks) {
        let pix = src.map(|v| NumCast::from(FloatNearest(v)).unwrap());
        *dst = *Pixel::from_slice(&pix);
    }

    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<I, P, S>(image: &I, sigma: f32, threshold: i32) -> ImageBuffer<P, Vec<S>>
where
    I: GenericImageView<Pixel = P>,
    P: Pixel<Subpixel = S>,
    S: Primitive,
{
    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 mut resized = img.clone();
        resized.resize(img.width(), img.height(), FilterType::Triangle);
        assert!(img.pixels().eq(resized.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.clone()).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;
        use crate::math::Rect;
        use crate::RgbaImage;

        fn assert_resize(image: &RgbaImage, filter: FilterType) {
            let resized = resize(image, 16, 16, filter);
            let select = Rect::from_xy_ranges(5..11, 5..11);
            let cropped = crop(&resized, select).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));
    }
}

Coverage Report

Created: 2026-03-20 07:09