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// 霍夫变换 16-bit/float 变体
// hough_lines / hough_circles
///|
/// 霍夫直线变换 16-bit 版本。
pub fn hough_lines_16(
img : @types.Image16,
threshold : Int,
theta_resolution? : Float = 1.0F,
rho_resolution? : Float = 1.0F,
) -> Array[HoughLine] raise @types.LoadError {
// 输入验证
if img.width <= 0 || img.height <= 0 {
raise @types.LoadError::DecodeFailed(
"hough_lines_16: 图像尺寸必须大于0",
)
}
if img.channels != 1 && img.channels != 3 && img.channels != 4 {
raise @types.LoadError::DecodeFailed(
"hough_lines_16: 仅支持 channels=1/3/4",
)
}
let expected_size = img.width * img.height * img.channels * 2
if img.data.length() != expected_size {
raise @types.LoadError::DecodeFailed(
"hough_lines_16: 数据大小不匹配，期望 " +
expected_size.to_string() +
"，实际 " +
img.data.length().to_string(),
)
}
if threshold <= 0 {
raise @types.LoadError::DecodeFailed(
"hough_lines_16: threshold必须大于0",
)
}
if theta_resolution <= 0.0F {
raise @types.LoadError::DecodeFailed(
"hough_lines_16: theta_resolution必须大于0",
)
}
if rho_resolution <= 0.0F {
raise @types.LoadError::DecodeFailed(
"hough_lines_16: rho_resolution必须大于0",
)
}
let w = img.width
let h = img.height
let ch = img.channels
let n_theta = (180.0F / theta_resolution).to_int()
let diag = Float::from_int(w * w + h * h).sqrt()
let max_rho = (diag / rho_resolution).to_int() + 1
let n_rho = 2 * max_rho + 1
let accumulator : Array[Int] = Array::make(n_rho * n_theta, 0)
let cos_table : Array[Float] = Array::make(n_theta, 0.0F)
let sin_table : Array[Float] = Array::make(n_theta, 0.0F)
let angle_scale = theta_resolution * 3.141592653589793F / 180.0F
for t in 0..<n_theta {
let angle = Float::from_int(t) * angle_scale
cos_table[t] = @math.cosf(angle)
sin_table[t] = @math.sinf(angle)
}
let inv_rho_res = 1.0F / rho_resolution
let rho_max_idx = n_rho - 1
let data_int : Array[Int] = Array::make(w * h, 0)
for i in 0..<(w * h) {
data_int[i] = img.data[i * ch * 2].to_int() |
(img.data[i * ch * 2 + 1].to_int() << 8)
}
for y in 0..<h {
let fy = Float::from_int(y)
let row_base = y * w
for x in 0..<w {
if data_int[row_base + x] == 0 {
continue
}
let fx = Float::from_int(x)
for t in 0..<n_theta {
let rho = fx * cos_table[t] + fy * sin_table[t]
let raw_idx = (rho * inv_rho_res).to_int() + max_rho
let rho_idx = if raw_idx < 0 {
0
} else if raw_idx > rho_max_idx {
rho_max_idx
} else {
raw_idx
}
accumulator[rho_idx * n_theta + t] += 1
}
}
}
let lines : Array[HoughLine] = []
let max_rho_f = Float::from_int(max_rho)
for r in 0..<n_rho {
let row_base = r * n_theta
for t in 0..<n_theta {
let votes = accumulator[row_base + t]
if votes >= threshold {
let rho = (Float::from_int(r) - max_rho_f) * rho_resolution
let theta = Float::from_int(t) * angle_scale
lines.push(HoughLine::{ rho, theta, votes })
}
}
}
lines.sort_by(fn(a, b) { b.votes.compare(a.votes) })
lines
}
///|
/// 霍夫直线变换 float 版本。
pub fn hough_lines_f(
img : @types.ImageF,
threshold : Int,
theta_resolution? : Float = 1.0F,
rho_resolution? : Float = 1.0F,
) -> Array[HoughLine] raise @types.LoadError {
// 输入验证
if img.width <= 0 || img.height <= 0 {
raise @types.LoadError::DecodeFailed(
"hough_lines_f: 图像尺寸必须大于0",
)
}
if img.channels != 1 && img.channels != 3 && img.channels != 4 {
raise @types.LoadError::DecodeFailed(
"hough_lines_f: 仅支持 channels=1/3/4",
)
}
let expected_size = img.width * img.height * img.channels * 4
if img.data.length() != expected_size {
raise @types.LoadError::DecodeFailed(
"hough_lines_f: 数据大小不匹配，期望 " +
expected_size.to_string() +
"，实际 " +
img.data.length().to_string(),
)
}
if threshold <= 0 {
raise @types.LoadError::DecodeFailed(
"hough_lines_f: threshold必须大于0",
)
}
if theta_resolution <= 0.0F {
raise @types.LoadError::DecodeFailed(
"hough_lines_f: theta_resolution必须大于0",
)
}
if rho_resolution <= 0.0F {
raise @types.LoadError::DecodeFailed(
"hough_lines_f: rho_resolution必须大于0",
)
}
let w = img.width
let h = img.height
let ch = img.channels
let n_theta = (180.0F / theta_resolution).to_int()
let diag = Float::from_int(w * w + h * h).sqrt()
let max_rho = (diag / rho_resolution).to_int() + 1
let n_rho = 2 * max_rho + 1
let accumulator : Array[Int] = Array::make(n_rho * n_theta, 0)
let cos_table : Array[Float] = Array::make(n_theta, 0.0F)
let sin_table : Array[Float] = Array::make(n_theta, 0.0F)
let angle_scale = theta_resolution * 3.141592653589793F / 180.0F
for t in 0..<n_theta {
let angle = Float::from_int(t) * angle_scale
cos_table[t] = @math.cosf(angle)
sin_table[t] = @math.sinf(angle)
}
let inv_rho_res = 1.0F / rho_resolution
let rho_max_idx = n_rho - 1
let data_f : Array[Float] = Array::make(w * h, 0.0F)
for i in 0..<(w * h) {
let bits = img.data[i * ch * 4].to_int() |
(img.data[i * ch * 4 + 1].to_int() << 8) |
(img.data[i * ch * 4 + 2].to_int() << 16) |
(img.data[i * ch * 4 + 3].to_int() << 24)
data_f[i] = Float::reinterpret_from_int(bits)
}
for y in 0..<h {
let fy = Float::from_int(y)
let row_base = y * w
for x in 0..<w {
if data_f[row_base + x] == 0.0F {
continue
}
let fx = Float::from_int(x)
for t in 0..<n_theta {
let rho = fx * cos_table[t] + fy * sin_table[t]
let raw_idx = (rho * inv_rho_res).to_int() + max_rho
let rho_idx = if raw_idx < 0 {
0
} else if raw_idx > rho_max_idx {
rho_max_idx
} else {
raw_idx
}
accumulator[rho_idx * n_theta + t] += 1
}
}
}
let lines : Array[HoughLine] = []
let max_rho_f = Float::from_int(max_rho)
for r in 0..<n_rho {
let row_base = r * n_theta
for t in 0..<n_theta {
let votes = accumulator[row_base + t]
if votes >= threshold {
let rho = (Float::from_int(r) - max_rho_f) * rho_resolution
let theta = Float::from_int(t) * angle_scale
lines.push(HoughLine::{ rho, theta, votes })
}
}
}
lines.sort_by(fn(a, b) { b.votes.compare(a.votes) })
lines
}