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// src/edge/morphology.rs
use std::time::Instant;
/// 形态学操作工具集
pub struct Morphology;
impl Morphology {
/// 对二值边缘图执行膨胀操作（Dilation），用于填补 Canny 输出中的边缘断点。
///
/// # 算法说明
/// 使用 (2*radius+1)×(2*radius+1) 的矩形结构元素。
/// 对每个像素，若其邻域内存在任意非零值，则输出置为 255。
/// 膨胀后相邻的短边缘段会连通，使轮廓追踪能提取出完整的长轮廓。
///
/// # 参数
/// - `src`：输入二值图（0 或 255），长度为 width * height
/// - `width`、`height`：图像尺寸
/// - `radius`：膨胀半径（像素），推荐值 1~2
///
/// # 返回
/// 膨胀后的二值图，与输入等长
pub fn dilate(src: &[u8], width: usize, height: usize, radius: usize) -> Vec<u8> {
let start = Instant::now();
let mut dst = vec![0u8; width * height];
// 统计输入边缘像素数，用于日志对比
let src_edge_count = src.iter().filter(|&&v| v > 0).count();
for y in 0..height {
for x in 0..width {
// 检查以 (x,y) 为中心的邻域内是否存在边缘像素
'outer: for dy in 0..=(2 * radius) {
for dx in 0..=(2 * radius) {
// 将无符号偏移转换为有符号坐标，避免下溢
let ny = y as isize + dy as isize - radius as isize;
let nx = x as isize + dx as isize - radius as isize;
// 边界裁剪
if nx < 0 || ny < 0 || nx >= width as isize || ny >= height as isize {
continue;
}
let nidx = ny as usize * width + nx as usize;
if src[nidx] > 0 {
dst[y * width + x] = 255;
break 'outer;
}
}
}
}
}
let dst_edge_count = dst.iter().filter(|&&v| v > 0).count();
log::debug!(
"[Edge::Morphology] - Dilate done: radius={}, src_edges={}, dst_edges={}, \
growth_ratio={:.2}x. Elapsed: {}ms",
radius,
src_edge_count,
dst_edge_count,
if src_edge_count > 0 {
dst_edge_count as f32 / src_edge_count as f32
} else {
0.0
},
start.elapsed().as_millis()
);
dst
}
/// 对二值边缘图执行腐蚀操作（Erosion），与膨胀配合实现闭运算（Close）。
///
/// # 算法说明
/// 使用 (2*radius+1)×(2*radius+1) 的矩形结构元素。
/// 对每个像素，若其邻域内全部为非零值，则输出置为 255，否则为 0。
/// 先膨胀后腐蚀（闭运算）可填补断点同时抑制膨胀带来的边缘增厚。
pub fn erode(src: &[u8], width: usize, height: usize, radius: usize) -> Vec<u8> {
let start = Instant::now();
let mut dst = vec![0u8; width * height];
let src_edge_count = src.iter().filter(|&&v| v > 0).count();
for y in 0..height {
for x in 0..width {
let mut all_edge = true;
'outer: for dy in 0..=(2 * radius) {
for dx in 0..=(2 * radius) {
let ny = y as isize + dy as isize - radius as isize;
let nx = x as isize + dx as isize - radius as isize;
// 边界像素视为非边缘（保守策略，避免边界膨胀）
if nx < 0 || ny < 0 || nx >= width as isize || ny >= height as isize {
all_edge = false;
break 'outer;
}
let nidx = ny as usize * width + nx as usize;
if src[nidx] == 0 {
all_edge = false;
break 'outer;
}
}
}
if all_edge {
dst[y * width + x] = 255;
}
}
}
let dst_edge_count = dst.iter().filter(|&&v| v > 0).count();
log::debug!(
"[Edge::Morphology] - Erode done: radius={}, src_edges={}, dst_edges={}. Elapsed: {}ms",
radius,
src_edge_count,
dst_edge_count,
start.elapsed().as_millis()
);
dst
}
/// 形态学闭运算（Close = Dilate → Erode），填补边缘断点并恢复边缘宽度。
///
/// # 参数
/// - `src`：输入二值边缘图
/// - `width`、`height`：图像尺寸
/// - `radius`：结构元素半径，推荐值 1（轻度填补）或 2（强力填补）
pub fn close(src: &[u8], width: usize, height: usize, radius: usize) -> Vec<u8> {
let start = Instant::now();
log::debug!(
"[Edge::Morphology] - Close operation start: {}x{}, radius={}",
width,
height,
radius
);
let dilated = Self::dilate(src, width, height, radius);
let closed = Self::erode(&dilated, width, height, radius);
let src_edges = src.iter().filter(|&&v| v > 0).count();
let closed_edges = closed.iter().filter(|&&v| v > 0).count();
log::info!(
"[Edge::Morphology] - Close done: src_edges={}, closed_edges={}, \
net_change={:+}. Elapsed: {}ms",
src_edges,
closed_edges,
closed_edges as i64 - src_edges as i64,
start.elapsed().as_millis()
);
closed
}
}