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// 自适应阈值（纯 MoonBit）
///|
/// 自适应阈值（均值法）。以每个像素周围 block_size×block_size 区域的均值减去 C 作为局部阈值。
/// 仅处理灰度图（channels=1）或 RGB 图（channels=3/4），alpha 通道不变。
pub fn adaptive_threshold_mean(
img : @types.Image,
block_size : Int,
c : Int,
) -> @types.Image raise @types.LoadError {
// 输入验证
if img.width <= 0 || img.height <= 0 {
raise @types.LoadError::DecodeFailed(
"adaptive_threshold_mean: 图像尺寸必须大于0",
)
}
if img.channels != 1 && img.channels != 3 && img.channels != 4 {
raise @types.LoadError::DecodeFailed(
"adaptive_threshold_mean 仅支持 channels=1/3/4",
)
}
let expected_size = img.width * img.height * img.channels
if img.data.length() != expected_size {
raise @types.LoadError::DecodeFailed(
"adaptive_threshold_mean: 数据大小不匹配，期望 " +
expected_size.to_string() +
"，实际 " +
img.data.length().to_string(),
)
}
if block_size < 3 || block_size % 2 == 0 {
raise @types.LoadError::DecodeFailed("block_size 必须为 >=3 的奇数")
}
let ch = img.channels
let color_ch = if ch >= 3 { 3 } else { 1 }
let w = img.width
let h = img.height
let n = w * h
let out : Array[Byte] = Array::make(n * ch, b'\x00')
let half = block_size / 2
// 预计算像素 Int 数组
let src : Array[Int] = Array::make(n * ch, 0)
for i in 0..<(n * ch) {
src[i] = img.data[i].to_int()
}
// 内部区域 count 恒为 block_size²
let inner_count = block_size * block_size
for c_idx in 0..<color_ch {
// 内部区域：x in [half, w-half), y in [half, h-half)
for y in half..<(h - half) {
for x in half..<(w - half) {
let mut sum = 0
for dy in -half..<=half {
let row = (y + dy) * w
for dx in -half..<=half {
sum += src[(row + x + dx) * ch + c_idx]
}
}
let mean = sum / inner_count
let threshold = mean - c
let v = src[(y * w + x) * ch + c_idx]
out[(y * w + x) * ch + c_idx] = if v > threshold {
b'\xFF'
} else {
b'\x00'
}
}
}
// 边界区域
for y in 0..<h {
let y_inner = y >= half && y < h - half
for x in 0..<w {
if y_inner && x >= half && x < w - half {
continue
}
let mut sum = 0
let mut count = 0
for dy in -half..<=half {
for dx in -half..<=half {
let ny = y + dy
let nx = x + dx
if ny >= 0 && ny < h && nx >= 0 && nx < w {
sum += src[(ny * w + nx) * ch + c_idx]
count += 1
}
}
}
let mean = if count > 0 { sum / count } else { 0 }
let threshold = mean - c
let v = src[(y * w + x) * ch + c_idx]
out[(y * w + x) * ch + c_idx] = if v > threshold {
b'\xFF'
} else {
b'\x00'
}
}
}
}
if ch == 4 {
for i in 0..<n {
out[i * 4 + 3] = img.data[i * 4 + 3]
}
}
@types.Image::{
width: w,
height: h,
channels: ch,
data: Bytes::from_array(out),
}
}
///|
/// 自适应阈值（高斯加权法）。以每个像素周围 block_size×block_size 区域的高斯加权均值减去 C 作为局部阈值。
pub fn adaptive_threshold_gaussian(
img : @types.Image,
block_size : Int,
c : Int,
) -> @types.Image raise @types.LoadError {
// 输入验证
if img.width <= 0 || img.height <= 0 {
raise @types.LoadError::DecodeFailed(
"adaptive_threshold_gaussian: 图像尺寸必须大于0",
)
}
if img.channels != 1 && img.channels != 3 && img.channels != 4 {
raise @types.LoadError::DecodeFailed(
"adaptive_threshold_gaussian 仅支持 channels=1/3/4",
)
}
let expected_size = img.width * img.height * img.channels
if img.data.length() != expected_size {
raise @types.LoadError::DecodeFailed(
"adaptive_threshold_gaussian: 数据大小不匹配，期望 " +
expected_size.to_string() +
"，实际 " +
img.data.length().to_string(),
)
}
if block_size < 3 || block_size % 2 == 0 {
raise @types.LoadError::DecodeFailed("block_size 必须为 >=3 的奇数")
}
let ch = img.channels
let color_ch = if ch >= 3 { 3 } else { 1 }
let w = img.width
let h = img.height
let n = w * h
let out : Array[Byte] = Array::make(n * ch, b'\x00')
let half = block_size / 2
let sigma = Float::from_int(half) / 2.0F
let two_sigma_sq = 2.0F * sigma * sigma
// 预计算高斯权重（1D 扁平化）
let weights : Array[Float] = Array::make(block_size * block_size, 0.0F)
let mut total_weight = 0.0F
for dy in 0..<block_size {
for dx in 0..<block_size {
let fy = Float::from_int(dy - half)
let fx = Float::from_int(dx - half)
let wt = @math.expf(-(fx * fx + fy * fy) / two_sigma_sq)
weights[dy * block_size + dx] = wt
total_weight += wt
}
}
// 预计算像素 Float 数组
let src : Array[Float] = Array::make(n * ch, 0.0F)
for i in 0..<(n * ch) {
src[i] = Float::from_int(img.data[i].to_int())
}
for c_idx in 0..<color_ch {
// 内部区域：weight_sum 恒为 total_weight
for y in half..<(h - half) {
for x in half..<(w - half) {
let mut sum = 0.0F
for dy in 0..<block_size {
let row = (y + dy - half) * w
let wbase = dy * block_size
for dx in 0..<block_size {
sum += src[(row + x + dx - half) * ch + c_idx] * weights[wbase + dx]
}
}
let mean = sum / total_weight
let threshold = mean - Float::from_int(c)
let v = src[(y * w + x) * ch + c_idx]
out[(y * w + x) * ch + c_idx] = if v > threshold {
b'\xFF'
} else {
b'\x00'
}
}
}
// 边界区域
for y in 0..<h {
let y_inner = y >= half && y < h - half
for x in 0..<w {
if y_inner && x >= half && x < w - half {
continue
}
let mut sum = 0.0F
let mut weight_sum = 0.0F
for dy in -half..<=half {
for dx in -half..<=half {
let ny = y + dy
let nx = x + dx
if ny >= 0 && ny < h && nx >= 0 && nx < w {
let weight = weights[(dy + half) * block_size + (dx + half)]
sum += src[(ny * w + nx) * ch + c_idx] * weight
weight_sum += weight
}
}
}
let mean = if weight_sum > 0.0F { sum / weight_sum } else { 0.0F }
let threshold = mean - Float::from_int(c)
let v = src[(y * w + x) * ch + c_idx]
out[(y * w + x) * ch + c_idx] = if v > threshold {
b'\xFF'
} else {
b'\x00'
}
}
}
}
if ch == 4 {
for i in 0..<n {
out[i * 4 + 3] = img.data[i * 4 + 3]
}
}
@types.Image::{
width: w,
height: h,
channels: ch,
data: Bytes::from_array(out),
}
}
///|
/// Otsu 大津法自动阈值。自动计算最佳全局阈值并二值化。
pub fn threshold_otsu(
img : @types.Image,
) -> @types.Image raise @types.LoadError {
// 输入验证
if img.width <= 0 || img.height <= 0 {
raise @types.LoadError::DecodeFailed(
"threshold_otsu: 图像尺寸必须大于0",
)
}
if img.channels != 1 && img.channels != 3 && img.channels != 4 {
raise @types.LoadError::DecodeFailed(
"threshold_otsu 仅支持 channels=1/3/4",
)
}
let expected_size = img.width * img.height * img.channels
if img.data.length() != expected_size {
raise @types.LoadError::DecodeFailed(
"threshold_otsu: 数据大小不匹配，期望 " +
expected_size.to_string() +
"，实际 " +
img.data.length().to_string(),
)
}
let ch = img.channels
let color_ch = if ch >= 3 { 3 } else { 1 }
let w = img.width
let h = img.height
let n = w * h
let out : Array[Byte] = Array::make(n * ch, b'\x00')
for c_idx in 0..<color_ch {
let hist : Array[Int] = Array::make(256, 0)
for i in 0..<n {
hist[img.data[i * ch + c_idx].to_int()] += 1
}
let mut total = 0
let mut sum = 0
for t in 0..<256 {
total += hist[t]
sum += t * hist[t]
}
let mut best_t = 0
let mut best_var = -1.0F
let mut w0 = 0
let mut sum0 = 0
for t in 0..<256 {
w0 += hist[t]
sum0 += t * hist[t]
if w0 == 0 || w0 == total {
continue
}
let w1 = total - w0
let sum1 = sum - sum0
let mean0 = Float::from_int(sum0) / Float::from_int(w0)
let mean1 = Float::from_int(sum1) / Float::from_int(w1)
let var_between = Float::from_int(w0) *
Float::from_int(w1) *
(mean0 - mean1) *
(mean0 - mean1)
if var_between > best_var {
best_var = var_between
best_t = t
}
}
for i in 0..<n {
let v = img.data[i * ch + c_idx].to_int()
out[i * ch + c_idx] = if v > best_t { b'\xFF' } else { b'\x00' }
}
}
if ch == 4 {
for i in 0..<n {
out[i * 4 + 3] = img.data[i * 4 + 3]
}
}
@types.Image::{
width: w,
height: h,
channels: ch,
data: Bytes::from_array(out),
}
}