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// 图像统计信息 16-bit/float 变体：compute_stats / mean_value
// 支持 Image16 / ImageF 两种类型
///|
fn read_u16_le_s(data : Bytes, offset : Int) -> Int {
data[offset].to_int() + data[offset + 1].to_int() * 256
}
///|
fn read_f32_le_s(data : Bytes, offset : Int) -> Float {
let b0 = data[offset].to_int()
let b1 = data[offset + 1].to_int()
let b2 = data[offset + 2].to_int()
let b3 = data[offset + 3].to_int()
let bits = b0 | (b1 << 8) | (b2 << 16) | (b3 << 24)
Float::reinterpret_from_int(bits)
}
///|
/// float 图像统计结果
pub(all) struct ImageStatsF {
mean : Float
stddev : Float
min_val : Float
max_val : Float
} derive(Eq, @debug.Debug)
///|
/// 计算 16-bit 图像统计信息（均值、标准差、最小值、最大值）。
/// 统计所有通道的像素值，像素值范围为 [0, 65535]。
pub fn compute_stats_16(img : @types.Image16) -> ImageStats {
let n = img.width * img.height * img.channels
let bpp = 2
let mut sum = 0
let mut sum_sq = 0L
let mut min_v = 65535
let mut max_v = 0
for i in 0..<n {
let v = read_u16_le_s(img.data, i * bpp)
sum += v
sum_sq += v.to_int64() * v.to_int64()
if v < min_v {
min_v = v
}
if v > max_v {
max_v = v
}
}
let mean = Float::from_int(sum) / Float::from_int(n)
let variance = Float::from_int64(sum_sq) / Float::from_int(n) - mean * mean
let stddev = (if variance < 0.0F { 0.0F } else { variance }).sqrt()
ImageStats::{ mean, stddev, min_val: min_v, max_val: max_v }
}
///|
/// 计算 float 图像统计信息（均值、标准差、最小值、最大值）。
/// 统计所有通道的像素值。
pub fn compute_stats_f(img : @types.ImageF) -> ImageStatsF {
let n = img.width * img.height * img.channels
let bpp = 4
let mut sum = 0.0F
let mut sum_sq = 0.0F
let mut min_v = read_f32_le_s(img.data, 0)
let mut max_v = min_v
for i in 0..<n {
let v = read_f32_le_s(img.data, i * bpp)
sum += v
sum_sq += v * v
if v < min_v {
min_v = v
}
if v > max_v {
max_v = v
}
}
let n_f = Float::from_int(n)
let mean = sum / n_f
let variance = sum_sq / n_f - mean * mean
let stddev = (if variance < 0.0F { 0.0F } else { variance }).sqrt()
ImageStatsF::{ mean, stddev, min_val: min_v, max_val: max_v }
}
///|
/// 计算 16-bit 图像均值（所有通道所有像素的平均值）。
pub fn mean_value_16(img : @types.Image16) -> Float {
let n = img.width * img.height * img.channels
let bpp = 2
let mut sum = 0
for i in 0..<n {
sum += read_u16_le_s(img.data, i * bpp)
}
Float::from_int(sum) / Float::from_int(n)
}
///|
/// 计算 float 图像均值（所有通道所有像素的平均值）。
pub fn mean_value_f(img : @types.ImageF) -> Float {
let n = img.width * img.height * img.channels
let bpp = 4
let mut sum = 0.0F
for i in 0..<n {
sum += read_f32_le_s(img.data, i * bpp)
}
sum / Float::from_int(n)
}