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//! Multi-channel image histogram analysis.
//!
//! Provides per-channel statistics (min, max, mean, std-dev, entropy,
//! percentiles) and whole-image exposure checks.
// ---------------------------------------------------------------------------
// ChannelHistogram
// ---------------------------------------------------------------------------
/// Per-channel histogram data with summary statistics.
#[derive(Debug, Clone)]
pub struct ChannelHistogram {
/// Raw bin counts; `bins[v]` is the number of pixels with value `v`.
pub bins: [u32; 256],
/// Channel index (0-based).
pub channel: u8,
/// Minimum pixel value present in this channel.
pub min_value: u8,
/// Maximum pixel value present in this channel.
pub max_value: u8,
/// Mean pixel value.
pub mean: f64,
/// Standard deviation of pixel values.
pub std_dev: f64,
}
impl ChannelHistogram {
/// Compute a histogram for one channel of a packed multi-channel image.
///
/// # Arguments
///
/// * `data` - Raw pixel bytes for the whole image.
/// * `channel` - Zero-based channel index (e.g. 0=R, 1=G, 2=B).
/// * `stride` - Offset between the start of consecutive rows in bytes
/// (use `width * num_channels` for packed images).
/// * `num_channels` - Total number of interleaved channels per pixel.
#[must_use]
#[allow(clippy::manual_checked_ops)]
pub fn compute(data: &[u8], channel: u8, stride: usize, num_channels: usize) -> Self {
let ch = channel as usize;
let mut bins = [0u32; 256];
if num_channels == 0 || data.is_empty() {
return Self {
bins,
channel,
min_value: 0,
max_value: 0,
mean: 0.0,
std_dev: 0.0,
};
}
// Iterate over all pixels: stride may differ from num_channels for
// padded rows, but for packed images stride == width * num_channels.
// We iterate byte-by-byte and pick channel `ch` from each pixel.
let total_rows = data.len().checked_div(stride).unwrap_or(0);
let mut count = 0u64;
let mut sum = 0u64;
for row in 0..total_rows {
let row_start = row * stride;
let row_end = (row_start + stride).min(data.len());
let mut byte_idx = row_start + ch;
while byte_idx < row_end {
let v = data[byte_idx];
bins[v as usize] += 1;
count += 1;
sum += u64::from(v);
byte_idx += num_channels;
}
}
// Handle any remaining bytes when data.len() is not a multiple of stride
// (only relevant when stride == 0, handled above, or non-rectangular).
let mean = if count > 0 {
sum as f64 / count as f64
} else {
0.0
};
// Variance pass
let mut sq_sum = 0.0f64;
for row in 0..total_rows {
let row_start = row * stride;
let row_end = (row_start + stride).min(data.len());
let mut byte_idx = row_start + ch;
while byte_idx < row_end {
let v = f64::from(data[byte_idx]);
let d = v - mean;
sq_sum += d * d;
byte_idx += num_channels;
}
}
let std_dev = if count > 1 {
(sq_sum / count as f64).sqrt()
} else {
0.0
};
// Min / max from bins
let mut min_value = 0u8;
let mut max_value = 0u8;
let mut found_min = false;
for (i, &b) in bins.iter().enumerate() {
if b > 0 {
if !found_min {
min_value = i as u8;
found_min = true;
}
max_value = i as u8;
}
}
Self {
bins,
channel,
min_value,
max_value,
mean,
std_dev,
}
}
/// Return the pixel value at the given percentile.
///
/// # Arguments
///
/// * `p` - Percentile in `[0.0, 1.0]` (e.g. 0.5 for the median).
#[must_use]
pub fn percentile(&self, p: f64) -> u8 {
let total: u64 = self.bins.iter().map(|&b| u64::from(b)).sum();
if total == 0 {
return 0;
}
let target = (p.clamp(0.0, 1.0) * total as f64) as u64;
let mut cumulative = 0u64;
for (i, &count) in self.bins.iter().enumerate() {
cumulative += u64::from(count);
if cumulative > target {
return i as u8;
}
}
255
}
/// Compute the Shannon entropy of this channel's histogram (in bits).
///
/// Returns `0.0` for a uniform (all-same-value) distribution.
#[must_use]
#[allow(clippy::manual_checked_ops)]
pub fn entropy(&self) -> f64 {
let total: u64 = self.bins.iter().map(|&b| u64::from(b)).sum();
if total == 0 {
return 0.0;
}
let total_f = total as f64;
self.bins
.iter()
.filter(|&&b| b > 0)
.map(|&b| {
let p = f64::from(b) / total_f;
-p * p.log2()
})
.sum()
}
}
// ---------------------------------------------------------------------------
// ImageHistogram
// ---------------------------------------------------------------------------
/// Multi-channel image histogram.
#[derive(Debug, Clone)]
pub struct ImageHistogram {
/// Per-channel histogram data.
pub channels: Vec<ChannelHistogram>,
/// Image width in pixels.
pub width: u32,
/// Image height in pixels.
pub height: u32,
}
impl ImageHistogram {
/// Compute a histogram for a packed RGB or RGBA image.
///
/// The number of channels is inferred from `data.len() / (width * height)`.
/// For standard packed RGB use 3 channels; for RGBA use 4.
#[must_use]
pub fn from_rgb(data: &[u8], width: u32, height: u32) -> Self {
let pixels = (width * height) as usize;
let num_channels = data.len().checked_div(pixels).unwrap_or(3);
let stride = width as usize * num_channels;
let channels = (0..num_channels as u8)
.map(|ch| ChannelHistogram::compute(data, ch, stride, num_channels))
.collect();
Self {
channels,
width,
height,
}
}
/// Compute a histogram for a single-channel (grayscale) image.
#[must_use]
pub fn from_gray(data: &[u8], width: u32, height: u32) -> Self {
let stride = width as usize;
let ch = ChannelHistogram::compute(data, 0, stride, 1);
Self {
channels: vec![ch],
width,
height,
}
}
/// Return `true` if any channel has a mean below 64 (underexposed).
#[must_use]
pub fn is_underexposed(&self) -> bool {
self.channels.iter().any(|ch| ch.mean < 64.0)
}
/// Return `true` if any channel has a mean above 192 (overexposed).
#[must_use]
pub fn is_overexposed(&self) -> bool {
self.channels.iter().any(|ch| ch.mean > 192.0)
}
/// Return the index of the channel with the highest mean pixel value.
#[must_use]
pub fn dominant_channel(&self) -> u8 {
self.channels
.iter()
.enumerate()
.max_by(|(_, a), (_, b)| {
a.mean
.partial_cmp(&b.mean)
.unwrap_or(std::cmp::Ordering::Equal)
})
.map_or(0, |(i, _)| i as u8)
}
}
// ---------------------------------------------------------------------------
// Tests
// ---------------------------------------------------------------------------
#[cfg(test)]
mod tests {
use super::*;
// ------------------------------------------------------------------
// ChannelHistogram tests
// ------------------------------------------------------------------
#[test]
fn test_compute_mean_single_channel() {
// 4 pixels, all value 128.
let data = vec![128u8; 4];
let hist = ChannelHistogram::compute(&data, 0, 4, 1);
assert!((hist.mean - 128.0).abs() < 1e-9);
assert_eq!(hist.bins[128], 4);
assert_eq!(hist.min_value, 128);
assert_eq!(hist.max_value, 128);
}
#[test]
fn test_compute_mean_rgb() {
// 2 pixels: [255, 0, 0, 255, 0, 0] → red channel mean = 255, green = 0.
let data = vec![255u8, 0, 0, 255, 0, 0];
let hist_r = ChannelHistogram::compute(&data, 0, 6, 3);
let hist_g = ChannelHistogram::compute(&data, 1, 6, 3);
assert!((hist_r.mean - 255.0).abs() < 1e-9);
assert!((hist_g.mean - 0.0).abs() < 1e-9);
}
#[test]
fn test_entropy_uniform_image_is_zero() {
// All pixels the same value → only one bin filled → entropy = 0.
let data = vec![200u8; 100];
let hist = ChannelHistogram::compute(&data, 0, 100, 1);
assert!(
hist.entropy() < 1e-9,
"entropy of uniform image should be ~0"
);
}
#[test]
fn test_entropy_two_equally_likely_values() {
// 50 pixels at 0 and 50 pixels at 255 → entropy ≈ 1.0 bit.
let mut data = vec![0u8; 100];
for b in data.iter_mut().take(50) {
*b = 255;
}
let hist = ChannelHistogram::compute(&data, 0, 100, 1);
let e = hist.entropy();
assert!((e - 1.0).abs() < 1e-6, "expected ~1.0 bit entropy, got {e}");
}
#[test]
fn test_percentile_median() {
// 100 pixels: 50 at 0, 50 at 255.
let mut data = vec![0u8; 100];
for i in 50..100 {
data[i] = 255;
}
let hist = ChannelHistogram::compute(&data, 0, 100, 1);
// 50th percentile should be 0 (cumulative count at 0 reaches target).
let p50 = hist.percentile(0.5);
// Exactly half the pixels are 0, so the 50th percentile lands at 0 or 255
// depending on rounding; just assert it's one of the two values.
assert!(
p50 == 0 || p50 == 255,
"median should be 0 or 255, got {p50}"
);
}
#[test]
fn test_std_dev_constant_image() {
let data = vec![100u8; 64];
let hist = ChannelHistogram::compute(&data, 0, 64, 1);
assert!(hist.std_dev < 1e-9, "std_dev of constant image should be 0");
}
// ------------------------------------------------------------------
// ImageHistogram tests
// ------------------------------------------------------------------
#[test]
fn test_from_rgb_2x2() {
// 2×2 image: all pixels (255, 0, 128)
let data: Vec<u8> = (0..4).flat_map(|_| vec![255u8, 0u8, 128u8]).collect();
let img = ImageHistogram::from_rgb(&data, 2, 2);
assert_eq!(img.channels.len(), 3);
assert!((img.channels[0].mean - 255.0).abs() < 1e-9);
assert!((img.channels[1].mean - 0.0).abs() < 1e-9);
assert!((img.channels[2].mean - 128.0).abs() < 1e-9);
}
#[test]
fn test_underexposed_detection() {
// All pixels at 10 → mean = 10, well below 64.
let data = vec![10u8; 100];
let img = ImageHistogram::from_gray(&data, 10, 10);
assert!(img.is_underexposed());
assert!(!img.is_overexposed());
}
#[test]
fn test_overexposed_detection() {
// All pixels at 250 → mean = 250, well above 192.
let data = vec![250u8; 100];
let img = ImageHistogram::from_gray(&data, 10, 10);
assert!(img.is_overexposed());
assert!(!img.is_underexposed());
}
#[test]
fn test_normal_exposure_neither() {
// All pixels at 128.
let data = vec![128u8; 100];
let img = ImageHistogram::from_gray(&data, 10, 10);
assert!(!img.is_underexposed());
assert!(!img.is_overexposed());
}
#[test]
fn test_dominant_channel() {
// Red=200, Green=50, Blue=100 → dominant = Red (channel 0).
let data: Vec<u8> = (0..4).flat_map(|_| vec![200u8, 50u8, 100u8]).collect();
let img = ImageHistogram::from_rgb(&data, 2, 2);
assert_eq!(img.dominant_channel(), 0);
}
#[test]
fn test_from_gray_single_channel() {
let data = vec![77u8; 25];
let img = ImageHistogram::from_gray(&data, 5, 5);
assert_eq!(img.channels.len(), 1);
assert!((img.channels[0].mean - 77.0).abs() < 1e-9);
}
#[test]
fn test_underexposed_rgb_one_channel_low() {
// Red=128, Green=128, Blue=10 → Blue < 64 → underexposed.
let data: Vec<u8> = (0..4).flat_map(|_| vec![128u8, 128u8, 10u8]).collect();
let img = ImageHistogram::from_rgb(&data, 2, 2);
assert!(img.is_underexposed());
}
}