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// 图像修复（纯 MoonBit）
// 基于扩散法：对掩码区域求解 Laplace 方程
// 已知像素作为 Dirichlet 边界条件，迭代至收敛
// 参考: Bertalmio et al., "Image Inpainting" (2000)
///|
/// 图像修复。
/// mask 为单通道掩码图像：0=已知像素，非0=待修复像素。
/// iterations 控制最大迭代次数（默认100）。
/// 适用于灰度图（channels=1）或 RGB/RGBA 图（channels=3/4）。
pub fn inpaint(
img : @types.Image,
mask : @types.Image,
iterations? : Int = 100,
) -> @types.Image raise @types.LoadError {
// 输入验证
if img.width <= 0 || img.height <= 0 {
raise @types.LoadError::DecodeFailed("inpaint: 图像尺寸必须大于0")
}
if img.channels < 1 || img.channels > 4 {
raise @types.LoadError::DecodeFailed("inpaint: 通道数必须在1-4之间")
}
let expected_size = img.width * img.height * img.channels
if img.data.length() != expected_size {
raise @types.LoadError::DecodeFailed(
"inpaint: 图像数据大小不匹配，期望 " +
expected_size.to_string() +
"，实际 " +
img.data.length().to_string(),
)
}
if img.width != mask.width || img.height != mask.height {
raise @types.LoadError::DecodeFailed(
"inpaint: 掩码尺寸必须与图像一致",
)
}
if mask.channels != 1 {
raise @types.LoadError::DecodeFailed(
"inpaint: 掩码必须为单通道图像",
)
}
let mask_expected_size = mask.width * mask.height
if mask.data.length() != mask_expected_size {
raise @types.LoadError::DecodeFailed(
"inpaint: 掩码数据大小不匹配，期望 " +
mask_expected_size.to_string() +
"，实际 " +
mask.data.length().to_string(),
)
}
if iterations <= 0 {
raise @types.LoadError::DecodeFailed("inpaint: iterations必须大于0")
}
let w = img.width
let h = img.height
let ch = img.channels
let n = w * h
// 构建待修复标记
let unknown : Array[Bool] = Array::make(n, false)
let mut has_unknown = false
for i in 0..<n {
if mask.data[i] != b'\x00' {
unknown[i] = true
has_unknown = true
}
}
// 若无待修复像素，直接返回原图
if !has_unknown {
return img
}
// 复制原图数据
let out : Array[Int] = Array::make(n * ch, 0)
for i in 0..<(n * ch) {
out[i] = img.data[i].to_int()
}
// Jacobi 迭代求解 Laplace 方程
for _ in 0..<iterations {
let prev = out.copy()
let mut changed = false
for y in 0..<h {
for x in 0..<w {
let idx = y * w + x
if !unknown[idx] {
continue
}
// 收集 8 邻域中已知/已更新像素
for c in 0..<ch {
let mut sum = 0
let mut count = 0
for dy in -1..<=1 {
let ny = y + dy
if ny < 0 || ny >= h {
continue
}
for dx in -1..<=1 {
if dx == 0 && dy == 0 {
continue
}
let nx = x + dx
if nx < 0 || nx >= w {
continue
}
sum += prev[(ny * w + nx) * ch + c]
count += 1
}
}
if count > 0 {
let new_val = sum / count
if new_val != out[idx * ch + c] {
changed = true
}
out[idx * ch + c] = new_val
}
}
}
}
// 收敛检测
if !changed {
break
}
}
// 转换输出
let out_data : Array[Byte] = Array::make(n * ch, b'\x00')
for i in 0..<(n * ch) {
out_data[i] = out[i].clamp(min=0, max=255).to_byte()
}
@types.Image::{
width: w,
height: h,
channels: ch,
data: Bytes::from_array(out_data),
}
}
///|
/// 快速图像修复（基于距离加权平均）。
/// 对每个待修复像素，取周围已知像素的加权平均（距离越近权重越大）。
/// 比 inpaint 快但质量略低，适合小面积修复。
pub fn inpaint_fast(
img : @types.Image,
mask : @types.Image,
radius? : Int = 5,
) -> @types.Image raise @types.LoadError {
// 输入验证
if img.width <= 0 || img.height <= 0 {
raise @types.LoadError::DecodeFailed(
"inpaint_fast: 图像尺寸必须大于0",
)
}
if img.channels < 1 || img.channels > 4 {
raise @types.LoadError::DecodeFailed(
"inpaint_fast: 通道数必须在1-4之间",
)
}
let expected_size = img.width * img.height * img.channels
if img.data.length() != expected_size {
raise @types.LoadError::DecodeFailed(
"inpaint_fast: 图像数据大小不匹配，期望 " +
expected_size.to_string() +
"，实际 " +
img.data.length().to_string(),
)
}
if img.width != mask.width || img.height != mask.height {
raise @types.LoadError::DecodeFailed(
"inpaint_fast: 掩码尺寸必须与图像一致",
)
}
if mask.channels != 1 {
raise @types.LoadError::DecodeFailed(
"inpaint_fast: 掩码必须为单通道图像",
)
}
let mask_expected_size = mask.width * mask.height
if mask.data.length() != mask_expected_size {
raise @types.LoadError::DecodeFailed(
"inpaint_fast: 掩码数据大小不匹配，期望 " +
mask_expected_size.to_string() +
"，实际 " +
mask.data.length().to_string(),
)
}
if radius <= 0 {
raise @types.LoadError::DecodeFailed("inpaint_fast: radius必须大于0")
}
let w = img.width
let h = img.height
let ch = img.channels
let n = w * h
let r = radius
// 复制原图数据
let out : Array[Int] = Array::make(n * ch, 0)
for i in 0..<(n * ch) {
out[i] = img.data[i].to_int()
}
// 对每个待修复像素，用已知像素加权平均
for y in 0..<h {
for x in 0..<w {
let idx = y * w + x
if mask.data[idx] == b'\x00' {
continue
}
for c in 0..<ch {
let mut sum = 0.0F
let mut weight_sum = 0.0F
for dy in -r..<=r {
let ny = y + dy
if ny < 0 || ny >= h {
continue
}
for dx in -r..<=r {
if dx == 0 && dy == 0 {
continue
}
let nx = x + dx
if nx < 0 || nx >= w {
continue
}
let nidx = ny * w + nx
// 只用已知像素
if mask.data[nidx] != b'\x00' {
continue
}
let dist = Float::from_int(dx * dx + dy * dy).sqrt()
let weight = 1.0F / (dist + 1.0F)
sum += weight * Float::from_int(out[nidx * ch + c])
weight_sum += weight
}
}
if weight_sum > 0.0F {
out[idx * ch + c] = (sum / weight_sum).to_int()
}
}
}
}
// 转换输出
let out_data : Array[Byte] = Array::make(n * ch, b'\x00')
for i in 0..<(n * ch) {
out_data[i] = out[i].clamp(min=0, max=255).to_byte()
}
@types.Image::{
width: w,
height: h,
channels: ch,
data: Bytes::from_array(out_data),
}
}