Struct image::math::nq::NeuQuant [−][src]
pub struct NeuQuant { /* fields omitted */ }
👎 Deprecated:
Use the color_quant
crate instead
Expand description
Neural network color quantizer
Examples
use image::imageops::colorops::{index_colors, ColorMap};
use image::math::nq::NeuQuant;
use image::{ImageBuffer, Rgba, RgbaImage};
// Create simple color image with RGBA pixels.
let (w, h) = (2, 2);
let red: Rgba<u8> = [255, 0, 0, 255].into();
let green: Rgba<u8> = [0, 255, 0, 255].into();
let blue: Rgba<u8> = [0, 0, 255, 255].into();
let white: Rgba<u8> = [255, 255, 255, 255].into();
let mut color_image = RgbaImage::new(w, h);
color_image.put_pixel(0, 0, red);
color_image.put_pixel(1, 0, green);
color_image.put_pixel(0, 1, blue);
color_image.put_pixel(1, 1, white);
// Create a `NeuQuant` colormap that will build an approximate color palette that best matches
// the original image.
// Note, the NeuQuant algorithm is only designed to work with 6-8 bit output, so `colors`
// should be a power of 2 in the range [64, 256].
let pixels = color_image.clone().into_raw();
let cmap = NeuQuant::new(1, 256, &pixels);
// Map the original image through the color map to create an indexed image stored in a
// `GrayImage`.
let palletized = index_colors(&color_image, &cmap);
// Map indexed image back `RgbaImage`. Note the NeuQuant algorithm creates an approximation of
// the original colors, so even in this simple example the output is not pixel equivalent to
// the original.
let mapped = ImageBuffer::from_fn(w, h, |x, y| -> Rgba<u8> {
let p = palletized.get_pixel(x, y);
cmap.lookup(p.0[0] as usize)
.expect("indexed color out-of-range")
.into()
});
Implementations
The implementation only calls the corresponding inner color_quant
methods.
These exist purely to keep a type separate from color_quant::NeuQuant
and the interface
stable for this major version. The type will be changed to a pure re-export in the next
version or might be removed.