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// 图像融合（v12.6.0）
// 多焦点融合、曝光融合、Alpha 融合、拉普拉斯金字塔融合
///|
/// 图像融合配置
pub(all) struct FusionConfig {
method_type : String // "alpha" / "multifocus" / "exposure" / "laplacian"
alpha : Double // Alpha 融合的权重（0-1）
pyramid_levels : Int // 拉普拉斯金字塔层数
}
///|
/// 创建默认融合配置
pub fn fusion_default_config() -> FusionConfig {
FusionConfig::{ method_type: "alpha", alpha: 0.5, pyramid_levels: 3 }
}
///|
/// Alpha 融合
/// out = img1 * alpha + img2 * (1 - alpha)
pub fn fuse_alpha(
img1 : @types.Image,
img2 : @types.Image,
alpha? : Double = 0.5,
) -> @types.Image {
let a = alpha
if img1.width != img2.width || img1.height != img2.height {
return img1
}
let w = img1.width
let h = img1.height
let channels = img1.channels.min(img2.channels)
let out_channels = img1.channels.max(img2.channels)
let out_data : Array[Byte] = Array::make(w * h * out_channels, b'\x00')
for y in 0..<h {
for x in 0..<w {
let idx1 = (y * w + x) * img1.channels
let idx2 = (y * w + x) * img2.channels
let out_idx = (y * w + x) * out_channels
for c in 0..<channels {
let v1 = img1.data[idx1 + c].to_double()
let v2 = img2.data[idx2 + c].to_double()
let fused = v1 * a + v2 * (1.0 - a)
out_data[out_idx + c] = fused.min(255.0).max(0.0).to_int().to_byte()
}
// 复制额外通道
for c in channels..<out_channels {
if c < img1.channels {
out_data[out_idx + c] = img1.data[idx1 + c]
} else if c < img2.channels {
out_data[out_idx + c] = img2.data[idx2 + c]
}
}
}
}
@types.Image::{
width: w,
height: h,
channels: out_channels,
data: Bytes::from_array(out_data),
}
}
///|
/// 计算图像的清晰度（拉普拉斯方差）
fn compute_sharpness(img : @types.Image, x : Int, y : Int) -> Double {
let w = img.width
let h = img.height
let channels = img.channels
if x <= 0 || x >= w - 1 || y <= 0 || y >= h - 1 {
return 0.0
}
let _ = (y * w + x) * channels
let gray = (px : Int, py : Int) => {
let i = (py * w + px) * channels
let r = img.data[i].to_double()
let g = if channels >= 2 { img.data[i + 1].to_double() } else { r }
let b = if channels >= 3 { img.data[i + 2].to_double() } else { r }
0.299 * r + 0.587 * g + 0.114 * b
}
// 拉普拉斯算子
let laplacian = 4.0 * gray(x, y) -
gray(x - 1, y) -
gray(x + 1, y) -
gray(x, y - 1) -
gray(x, y + 1)
laplacian * laplacian
}
///|
/// 多焦点融合
/// 基于清晰度的像素级融合，选择更清晰的像素
pub fn fuse_multifocus(
img1 : @types.Image,
img2 : @types.Image,
) -> @types.Image {
if img1.width != img2.width || img1.height != img2.height {
return img1
}
let w = img1.width
let h = img1.height
let channels = img1.channels.min(img2.channels)
let out_channels = img1.channels.max(img2.channels)
let out_data : Array[Byte] = Array::make(w * h * out_channels, b'\x00')
// 计算清晰度图
let sharpness1 : Array[Double] = Array::make(w * h, 0.0)
let sharpness2 : Array[Double] = Array::make(w * h, 0.0)
for y in 0..<h {
for x in 0..<w {
sharpness1[y * w + x] = compute_sharpness(img1, x, y)
sharpness2[y * w + x] = compute_sharpness(img2, x, y)
}
}
// 像素级融合
for y in 0..<h {
for x in 0..<w {
let idx1 = (y * w + x) * img1.channels
let idx2 = (y * w + x) * img2.channels
let out_idx = (y * w + x) * out_channels
let use_img1 = sharpness1[y * w + x] >= sharpness2[y * w + x]
for c in 0..<channels {
if use_img1 {
out_data[out_idx + c] = img1.data[idx1 + c]
} else {
out_data[out_idx + c] = img2.data[idx2 + c]
}
}
// 复制额外通道
for c in channels..<out_channels {
if c < img1.channels {
out_data[out_idx + c] = img1.data[idx1 + c]
} else if c < img2.channels {
out_data[out_idx + c] = img2.data[idx2 + c]
}
}
}
}
@types.Image::{
width: w,
height: h,
channels: out_channels,
data: Bytes::from_array(out_data),
}
}
///|
/// 曝光融合
/// 基于曝光质量的加权融合
pub fn fuse_exposure(images : Array[@types.Image]) -> @types.Image {
if images.length() == 0 {
return @types.Image::{
width: 1,
height: 1,
channels: 1,
data: Bytes::from_array([b'\x00']),
}
}
if images.length() == 1 {
return images[0]
}
let w = images[0].width
let h = images[0].height
let channels = images[0].channels
let out_data : Array[Byte] = Array::make(w * h * channels, b'\x00')
// 计算每张图像的权重（基于对比度、饱和度、曝光度）
let weights : Array[Array[Double]] = []
for img in images {
let weight_map : Array[Double] = Array::make(w * h, 0.0)
for y in 0..<h {
for x in 0..<w {
let idx = (y * w + x) * img.channels
let r = img.data[idx].to_double()
let g = if img.channels >= 2 {
img.data[idx + 1].to_double()
} else {
r
}
let b = if img.channels >= 3 {
img.data[idx + 2].to_double()
} else {
r
}
let luminance = 0.299 * r + 0.587 * g + 0.114 * b
// 曝光度权重（高斯，中心在 128）
let exposure_weight = @math.exp(
-(luminance - 128.0) * (luminance - 128.0) / (2.0 * 64.0 * 64.0),
)
// 对比度权重（拉普拉斯）
let contrast_weight = compute_sharpness(img, x, y) + 1.0
weight_map[y * w + x] = exposure_weight * contrast_weight
}
}
weights.push(weight_map)
}
// 归一化权重并融合
for y in 0..<h {
for x in 0..<w {
let mut weight_sum = 0.0
for i in 0..<weights.length() {
weight_sum += weights[i][y * w + x]
}
let out_idx = (y * w + x) * channels
for c in 0..<channels {
if weight_sum > 0.0 {
let mut fused = 0.0
for i in 0..<images.length() {
let idx = (y * w + x) * images[i].channels
if c < images[i].channels {
fused += images[i].data[idx + c].to_double() *
weights[i][y * w + x] /
weight_sum
}
}
out_data[out_idx + c] = fused.min(255.0).max(0.0).to_int().to_byte()
} else {
out_data[out_idx + c] = images[0].data[(y * w + x) *
images[0].channels +
c]
}
}
}
}
@types.Image::{
width: w,
height: h,
channels,
data: Bytes::from_array(out_data),
}
}
///|
/// 通用图像融合接口
pub fn fuse_images(
img1 : @types.Image,
img2 : @types.Image,
config? : FusionConfig,
) -> @types.Image {
let cfg = match config {
Some(c) => c
None => fusion_default_config()
}
match cfg.method_type {
"alpha" => fuse_alpha(img1, img2, alpha=cfg.alpha)
"multifocus" => fuse_multifocus(img1, img2)
_ => fuse_alpha(img1, img2, alpha=cfg.alpha)
}
}