Struct image::math::nq::NeuQuant [−][src]
pub struct NeuQuant { /* fields omitted */ }
👎 Deprecated:
Use the color_quant
crate instead
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
impl NeuQuant
[src]
impl NeuQuant
[src]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.