I have implemented a simple version of the median cut algorithm. It takes a vector of Color structs representing pixel in an image. I also use the ColorChanel enum representing RGBA channels.
pub struct Color {
pub r: u8,
pub g: u8,
pub b: u8,
pub a: u8,
}
#[derive(Debug)]
pub enum ColorChannel {
R,
G,
B,
A,
}
impl Color {
pub fn channel_val(&self, channel: &ColorChannel) -> u8 {
match channel {
ColorChannel::R => self.r,
ColorChannel::G => self.g,
ColorChannel::B => self.b,
ColorChannel::A => self.a,
}
}
}
impl PartialEq for Color {
fn eq(&self, other: &Self) -> bool {
self.r == other.r && self.g == other.g && self.b == other.b && self.a == other.a
}
}
For each Color/Pixel vector I look for the channel with the highest range:
/// Returns the color channel with the highest range.
/// IMPORTANT: Ignores alpha channel!
///
/// # Arguments
///
/// * `colors` - Color vector from which the highest range is evaluated.
///
fn highest_range_channel(colors: &Vec<Color>) -> Option<ColorChannel> {
if let Some(ranges) = color_ranges(colors) {
let mut highest_range_channel = ColorChannel::R;
let mut highest_value = ranges.r;
if ranges.g > highest_value {
highest_range_channel = ColorChannel::G;
highest_value = ranges.g;
}
if ranges.b > highest_value {
highest_range_channel = ColorChannel::B;
}
return Some(highest_range_channel);
}
None
}
The color ranges are calculated in the according function:
/// Returns the ranges for each color channel
///
/// # Arguments
///
/// * `colors` - Color vector from which the ranges are calculated.
///
/// # Examples
///
/// ```
/// let colors = Vec::<Color>::new();
/// let color_ranges_data = color_ranges(colors);
/// ```
///
fn color_ranges(colors: &Vec<Color>) -> Option<Color> {
if colors.is_empty() {
return None;
}
// Unwrap is ok here, because `max_by_key` only returns `None` for empty vectors
let r_range = colors.iter().max_by_key(|c| c.r).unwrap().r;
let g_range = colors.iter().max_by_key(|c| c.g).unwrap().g;
let b_range = colors.iter().max_by_key(|c| c.b).unwrap().b;
let a_range = colors.iter().max_by_key(|c| c.a).unwrap().a;
Some(Color {
r: r_range,
g: g_range,
b: b_range,
a: a_range,
})
}
After that I calculate the median value for the channel with the highest range. I am doing this by sorting the vector based on the desired channel and finding the value in the "middle" of the vector.
/// Sort a color vector for a specific channel.
///
/// # Arguments
///
/// * `colors` - Color data which will be sorted.
/// * `channel` - Target channel. The sorting is performed based on this value.
///
/// # Examples
///
/// ```
/// let mut colors = Vec::<Color>::new();
/// sort_colors(&mut colors, &ColorChannel::R);
/// ```
///
fn sort_colors(colors: &mut Vec<Color>, channel: &ColorChannel) {
if colors.is_empty() {
return;
}
match channel {
ColorChannel::R => colors.sort_by(|a, b| a.r.cmp(&b.r)),
ColorChannel::G => colors.sort_by(|a, b| a.g.cmp(&b.g)),
ColorChannel::B => colors.sort_by(|a, b| a.b.cmp(&b.b)),
ColorChannel::A => colors.sort_by(|a, b| a.a.cmp(&b.a)),
}
}
/// Returns median value for a specific `ColorChannel`.
///
/// # Arguments
///
/// * `colors` - Color vector from which the median value is calculated.
/// * `channel` - Target channel for which the median is calculated.
///
/// # Examples
/// ```
/// let mut colors = Vec::<Color>::new();
/// let mut result = color_median(&mut colors, &ColorChannel::R);
/// ```
///
fn color_median(colors: &mut Vec<Color>, channel: &ColorChannel) -> Option<u8> {
if colors.is_empty() {
return None;
}
sort_colors(colors, channel);
let mid = colors.len() / 2;
if colors.len() % 2 == 0 {
channel_mean(&vec![colors[mid - 1], colors[mid]], channel)
} else {
channel_value_by_index(colors, mid, channel)
}
}
/// Returns a color value based on the provided channel and index parameters.
///
/// # Arguments
///
/// * `colors` - Color vector from which the value is retreived.
/// * `index` - Index of the target color in the vector.
/// * `channel` - Color channel of the searched value.
///
/// # Examples
///
/// ```
/// let mut colors: Vec<Color> = Vec::new();
/// colors.push(Color { r: 100, g: 22, b: 12, a: 0 });
/// assert_eq!(Some(100), channel_value_by_index(&colors, 0, &ColorChannel::R));
/// ```
///
fn channel_value_by_index(colors: &Vec<Color>, index: usize, channel: &ColorChannel) -> Option<u8> {
if colors.is_empty() || index >= colors.len() {
return None;
}
match channel {
ColorChannel::R => Some(colors[index].r),
ColorChannel::G => Some(colors[index].g),
ColorChannel::B => Some(colors[index].b),
ColorChannel::A => Some(colors[index].a),
}
}
/// Calculate the mean value for a specific color channel on a vector of `Color`.
///
/// # Arguments
///
/// * `colors` - Color vector from which the mean value is calculated.
/// * `channel` - Target channel for which the mean is calculated.
///
/// # Examples
///
/// ```
/// let mut colors: Vec<Color> = Vec::new();
/// let mut result = channel_mean(&colors, &ColorChannel::R);
/// ```
///
fn channel_mean(colors: &Vec<Color>, channel: &ColorChannel) -> Option<u8> {
let number_colors = colors.len();
if number_colors == 0 {
return None;
}
match channel {
ColorChannel::R => Some((colors.iter().fold(0, |acc: u32, x| x.r as u32 + acc) / number_colors as u32) as u8),
ColorChannel::G => Some((colors.iter().fold(0, |acc: u32, x| x.g as u32 + acc) / number_colors as u32) as u8),
ColorChannel::B => Some((colors.iter().fold(0, |acc: u32, x| x.b as u32 + acc) / number_colors as u32) as u8),
ColorChannel::A => Some((colors.iter().fold(0, |acc: u32, x| x.a as u32 + acc) / number_colors as u32) as u8),
}
}
Now I create two new vectors/buckets with one containing all Colors above the median value and another with Color values below the median. This entire process is implemented in the median_cut function.
/// Performs the median cut on a single vector (bucket) of `Color`.
/// Returns two `color` vectors representing the colors above and colors below median value.
///
/// # Arguments
///
/// * `colors` - `Color` vector on which the median cut is performed.
///
fn median_cut(colors: &mut Vec<Color>) -> (Vec<Color>, Vec<Color>) {
if colors.is_empty() {
return (Vec::<Color>::new(), Vec::<Color>::new());
}
if let Some(highest_range_channel) = highest_range_channel(&colors) {
if let Some(median) = color_median(colors, &highest_range_channel) {
let mut above_median = Vec::<Color>::new();
let mut below_median = Vec::<Color>::new();
for color in colors {
if color.channel_val(&highest_range_channel) > median {
above_median.push(*color);
} else {
below_median.push(*color);
}
}
return (above_median, below_median);
}
}
return (Vec::<Color>::new(), Vec::<Color>::new());
}
In order to perform multiple iterations I call the recurse function. It takes a bucket, performs the median_cut on it calculates the mean color for each output bucket and performs the median_cut on the them. The function stops and returns all mean colors when the amount of iterations reaches 0.
pub fn recurse(bucket: &mut Vec<Color>, iter_count: u8) -> Option<Vec<Color>> {
if iter_count < 1 || bucket.is_empty() {
return None;
}
let mut result = Vec::<Color>::new();
let mut new_buckets = median_cut(bucket);
if !new_buckets.0.is_empty() {
if let Some(c_0) = color_mean(&new_buckets.0) {
result.push(c_0);
}
if let Some(new_colors) = recurse(&mut new_buckets.0, iter_count - 1) {
result.append(&mut new_colors.clone());
}
}
if !new_buckets.1.is_empty() {
if let Some(c_1) = color_mean(&new_buckets.1) {
result.push(c_1);
}
if let Some(new_colors) = recurse(&mut new_buckets.1, iter_count - 1) {
result.append(&mut new_colors.clone());
}
}
Some(result)
}
/// Returns the mean color value based on the passed colors.
///
/// # Arguments
///
/// * `colors` - Color vector from which the mean color is calculated.
///
/// # Examples
///
/// ```
/// let colors = Vec::<Color>::new();
/// let result = color_mean(&colors);
/// ```
///
fn color_mean(colors: &Vec<Color>) -> Option<Color> {
if colors.is_empty() {
return None;
}
let r_mean = (colors.iter().fold(0, |acc: u32, c| acc + c.r as u32) / colors.len() as u32) as u8;
let g_mean = (colors.iter().fold(0, |acc: u32, c| acc + c.g as u32) / colors.len() as u32) as u8;
let b_mean = (colors.iter().fold(0, |acc: u32, c| acc + c.b as u32) / colors.len() as u32) as u8;
let a_mean = (colors.iter().fold(0, |acc: u32, c| acc + c.a as u32) / colors.len() as u32) as u8;
Some(Color {
r: r_mean,
g: g_mean,
b: b_mean,
a: a_mean,
})
}
So basically a call to the entire algorithm looks like this:
let mut pixels = Vec::new();
// fill pixels with data
if let Some(colors) = recurse(&mut pixels, 3) {
// Do something with the output colors
}
I am very new to rust so my concerns about the code are:
- The usage of
Option. I second guess if I should have usedResultwith some meaningful error information. Also I am feeling like the "high level" usage of functions returning Options produces arrow-like code (like inmedian_cut) which I personally find hard to read. - Overall performance, it feels like there is a lot of iteration and cloning going on.
- Algorithm implementation, There are scenarios which return "interesting" results. E.g. if I use an image which only contains red and blue pixels, the algorithm returns a blue, a red and a purple result. As I understand it the algorithm should perform a color reduction.
How could I optimize the code based on the above topics?
