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// Harris 角点检测（纯 MoonBit）
///|
/// 角点信息
pub(all) struct CornerPoint {
x : Int
y : Int
response : Float // Harris 响应值
}
///|
/// Harris 角点检测。
/// 适用于灰度图（channels=1）或 RGB/RGBA 图（channels=3/4，自动转灰度）。
/// k 为 Harris 参数（默认 0.04），threshold 为响应阈值，min_distance 为角点间最小距离。
pub fn harris_corners(
img : @types.Image,
k? : Float = 0.04F,
threshold? : Float = 1000.0F,
min_distance? : Int = 5,
) -> Array[CornerPoint] raise @types.LoadError {
// 输入验证
if img.width <= 0 || img.height <= 0 {
raise @types.LoadError::DecodeFailed(
"harris_corners: 图像尺寸必须大于0",
)
}
if img.channels != 1 && img.channels != 3 && img.channels != 4 {
raise @types.LoadError::DecodeFailed(
"harris_corners: 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(
"harris_corners: 数据大小不匹配，期望 " +
expected_size.to_string() +
"，实际 " +
img.data.length().to_string(),
)
}
if k <= 0.0F {
raise @types.LoadError::DecodeFailed("harris_corners: k必须大于0")
}
if threshold < 0.0F {
raise @types.LoadError::DecodeFailed("harris_corners: threshold必须 >= 0")
}
if min_distance < 0 {
raise @types.LoadError::DecodeFailed(
"harris_corners: min_distance必须 >= 0",
)
}
let w = img.width
let h = img.height
let n = w * h
if n == 0 {
return []
}
// 转灰度
let gray : Array[Float] = Array::make(n, 0.0F)
let ch = img.channels
for i in 0..<n {
let v = if ch == 1 {
img.data[i].to_int()
} else {
let r = img.data[i * ch].to_int()
let g = img.data[i * ch + 1].to_int()
let b = img.data[i * ch + 2].to_int()
(r * 299 + g * 587 + b * 114) / 1000
}
gray[i] = Float::from_int(v)
}
// Sobel 梯度
let ix : Array[Float] = Array::make(n, 0.0F)
let iy : Array[Float] = Array::make(n, 0.0F)
for y in 1..<(h - 1) {
for x in 1..<(w - 1) {
let idx = y * w + x
ix[idx] = (
gray[(y - 1) * w + (x + 1)] +
2.0F * gray[y * w + (x + 1)] +
gray[(y + 1) * w + (x + 1)] -
gray[(y - 1) * w + (x - 1)] -
2.0F * gray[y * w + (x - 1)] -
gray[(y + 1) * w + (x - 1)]
) /
8.0F
iy[idx] = (
gray[(y + 1) * w + (x - 1)] +
2.0F * gray[(y + 1) * w + x] +
gray[(y + 1) * w + (x + 1)] -
gray[(y - 1) * w + (x - 1)] -
2.0F * gray[(y - 1) * w + x] -
gray[(y - 1) * w + (x + 1)]
) /
8.0F
}
}
// 计算 Ix², Iy², IxIy
let ixx : Array[Float] = Array::make(n, 0.0F)
let iyy : Array[Float] = Array::make(n, 0.0F)
let ixy : Array[Float] = Array::make(n, 0.0F)
for i in 0..<n {
ixx[i] = ix[i] * ix[i]
iyy[i] = iy[i] * iy[i]
ixy[i] = ix[i] * iy[i]
}
// 高斯加权（sigma=1, 3x3 核）
let sxx = harris_gaussian_blur(ixx, w, h)
let syy = harris_gaussian_blur(iyy, w, h)
let sxy = harris_gaussian_blur(ixy, w, h)
// Harris 响应: R = det(M) - k * trace(M)²
let response : Array[Float] = Array::make(n, 0.0F)
for i in 0..<n {
let det = sxx[i] * syy[i] - sxy[i] * sxy[i]
let trace = sxx[i] + syy[i]
response[i] = det - k * trace * trace
}
// 非极大值抑制 + 阈值
let candidates : Array[CornerPoint] = []
for y in 1..<(h - 1) {
for x in 1..<(w - 1) {
let idx = y * w + x
let r = response[idx]
if r < threshold {
continue
}
// 检查是否为局部最大值
let mut is_max = true
for dy in -1..<=1 {
for dx in -1..<=1 {
if dx == 0 && dy == 0 {
continue
}
if response[(y + dy) * w + (x + dx)] > r {
is_max = false
break
}
}
if !is_max {
break
}
}
if is_max {
candidates.push(CornerPoint::{ x, y, response: r })
}
}
}
// 按响应值排序（降序）
candidates.sort_by(fn(a, b) { b.response.compare(a.response) })
// 距离过滤
let result : Array[CornerPoint] = []
for c in candidates {
let mut ok = true
for r in result {
let dx = c.x - r.x
let dy = c.y - r.y
if dx * dx + dy * dy < min_distance * min_distance {
ok = false
break
}
}
if ok {
result.push(c)
}
}
result
}
///|
/// 3x3 高斯模糊（sigma=1.0），分离内部/边界区域以消除热点 clamp 调用
fn harris_gaussian_blur(data : Array[Float], w : Int, h : Int) -> Array[Float] {
let n = w * h
let out : Array[Float] = Array::make(n, 0.0F)
let kernel : Array[Float] = [
0.0625F, 0.125F, 0.0625F, 0.125F, 0.25F, 0.125F, 0.0625F, 0.125F, 0.0625F,
]
// 内部区域：x in [1, w-1), y in [1, h-1)，3x3 核完全在图像内
for y in 1..<(h - 1) {
for x in 1..<(w - 1) {
let base = (y - 1) * w + (x - 1)
out[y * w + x] = data[base] * kernel[0] +
data[base + 1] * kernel[1] +
data[base + 2] * kernel[2] +
data[base + w] * kernel[3] +
data[base + w + 1] * kernel[4] +
data[base + w + 2] * kernel[5] +
data[base + 2 * w] * kernel[6] +
data[base + 2 * w + 1] * kernel[7] +
data[base + 2 * w + 2] * kernel[8]
}
}
// 边界区域：需要 clamp
for y in 0..<h {
let y_inner = y >= 1 && y < h - 1
for x in 0..<w {
if y_inner && x >= 1 && x < w - 1 {
continue
}
let mut sum = 0.0F
for ky in 0..<3 {
for kx in 0..<3 {
let px = (x + kx - 1).clamp(min=0, max=w - 1)
let py = (y + ky - 1).clamp(min=0, max=h - 1)
sum += data[py * w + px] * kernel[ky * 3 + kx]
}
}
out[y * w + x] = sum
}
}
out
}
///|
/// 在图像上绘制角点。
pub fn draw_corners(
img : @types.Image,
corners : Array[CornerPoint],
color? : Array[Byte] = [b'\xFF', b'\x00', b'\x00'],
radius? : Int = 2,
) -> @types.Image raise @types.LoadError {
// 输入验证
if img.width <= 0 || img.height <= 0 {
raise @types.LoadError::DecodeFailed(
"draw_corners: 图像尺寸必须大于0",
)
}
if img.channels != 1 && img.channels != 3 && img.channels != 4 {
raise @types.LoadError::DecodeFailed(
"draw_corners: 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(
"draw_corners: 数据大小不匹配，期望 " +
expected_size.to_string() +
"，实际 " +
img.data.length().to_string(),
)
}
if color.length() == 0 {
raise @types.LoadError::DecodeFailed(
"draw_corners: color数组不能为空",
)
}
if radius < 0 {
raise @types.LoadError::DecodeFailed("draw_corners: radius必须 >= 0")
}
let ch = img.channels
let w = img.width
let h = img.height
let out : Array[Byte] = Array::make(w * h * ch, b'\x00')
for i in 0..<(w * h * ch) {
out[i] = img.data[i]
}
let draw_ch = if ch >= 3 { 3 } else { 1 }
for corner in corners {
for dy in -radius..<=radius {
for dx in -radius..<=radius {
let nx = corner.x + dx
let ny = corner.y + dy
if nx >= 0 && nx < w && ny >= 0 && ny < h {
for c in 0..<draw_ch {
if c < color.length() {
out[(ny * w + nx) * ch + c] = color[c]
}
}
}
}
}
}
@types.Image::{
width: w,
height: h,
channels: ch,
data: Bytes::from_array(out),
}
}