1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
// 边缘检测扩展（纯 MoonBit）：edge_detect_laplacian / edge_detect_prewitt / edge_detect_sobel
// clamp_b 来自 pixel_advanced.mbt，clamp_i 来自 morphology.mbt
///|
/// Sobel 边缘检测。先转灰度，然后计算梯度幅值。
/// 输出为单通道灰度图（channels=1）。
pub fn edge_detect_sobel(
img : @types.Image,
) -> @types.Image raise @types.LoadError {
// 输入验证
if img.width <= 0 || img.height <= 0 {
raise @types.LoadError::DecodeFailed(
"edge_detect_sobel: 图像尺寸必须大于0",
)
}
if img.channels < 1 || img.channels > 4 {
raise @types.LoadError::DecodeFailed(
"edge_detect_sobel: 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(
"edge_detect_sobel: 数据大小不匹配，期望 " +
expected_size.to_string() +
"，实际 " +
img.data.length().to_string(),
)
}
let w = img.width
let h = img.height
let ch = img.channels
let n = w * h
// 先转灰度
let gray : Array[Int] = Array::make(n, 0)
for i in 0..<n {
if ch == 1 {
gray[i] = 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()
gray[i] = (r * 299 + g * 587 + b * 114) / 1000
}
}
// Sobel 卷积
let out : Array[Byte] = Array::make(n, b'\x00')
// 内部区域：1 <= x < w-1 && 1 <= y < h-1，无需 clamp
for y in 1..<(h - 1) {
for x in 1..<(w - 1) {
let idx = y * w + x
let idx_l = idx - 1
let idx_r = idx + 1
let idx_t = idx - w
let idx_b = idx + w
// Gx = [-1 0 1; -2 0 2; -1 0 1]
let gx = -gray[idx_t - 1] +
gray[idx_t + 1] -
2 * gray[idx_l] +
2 * gray[idx_r] -
gray[idx_b - 1] +
gray[idx_b + 1]
// Gy = [-1 -2 -1; 0 0 0; 1 2 1]
let gy = -gray[idx_t - 1] -
2 * gray[idx_t] -
gray[idx_t + 1] +
gray[idx_b - 1] +
2 * gray[idx_b] +
gray[idx_b + 1]
let mag = Float::from_int(gx * gx + gy * gy).sqrt()
out[idx] = (mag + 0.5F).to_int().clamp(min=0, max=255).to_byte()
}
}
// 边界区域
for y in 0..<h {
for x in 0..<w {
if x >= 1 && x < w - 1 && y >= 1 && y < h - 1 {
continue
}
let x_l = clamp_i(x - 1, 0, w - 1)
let x_r = clamp_i(x + 1, 0, w - 1)
let y_t = clamp_i(y - 1, 0, h - 1)
let y_b = clamp_i(y + 1, 0, h - 1)
let gx = -gray[y_t * w + x_l] +
gray[y_t * w + x_r] -
2 * gray[y * w + x_l] +
2 * gray[y * w + x_r] -
gray[y_b * w + x_l] +
gray[y_b * w + x_r]
let gy = -gray[y_t * w + x_l] -
2 * gray[y_t * w + x] -
gray[y_t * w + x_r] +
gray[y_b * w + x_l] +
2 * gray[y_b * w + x] +
gray[y_b * w + x_r]
let mag = Float::from_int(gx * gx + gy * gy).sqrt()
out[y * w + x] = (mag + 0.5F).to_int().clamp(min=0, max=255).to_byte()
}
}
@types.Image::{
width: w,
height: h,
channels: 1,
data: Bytes::from_array(out),
}
}
///|
/// Laplacian 边缘检测。使用 3x3 Laplacian 核：
/// [0, 1, 0; 1, -4, 1; 0, 1, 0]
/// 返回灰度图（channels=1）。
pub fn edge_detect_laplacian(
img : @types.Image,
) -> @types.Image raise @types.LoadError {
// 输入验证
if img.width <= 0 || img.height <= 0 {
raise @types.LoadError::DecodeFailed(
"edge_detect_laplacian: 图像尺寸必须大于0",
)
}
if img.channels < 1 || img.channels > 4 {
raise @types.LoadError::DecodeFailed(
"edge_detect_laplacian: 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(
"edge_detect_laplacian: 数据大小不匹配，期望 " +
expected_size.to_string() +
"，实际 " +
img.data.length().to_string(),
)
}
let ch = img.channels
let w = img.width
let h = img.height
let n = w * h
let out : Array[Byte] = Array::make(n, b'\x00')
// 预计算灰度 Int 数组
let gray : Array[Int] = Array::make(n, 0)
for i in 0..<n {
if ch >= 3 {
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()
gray[i] = (r * 77 + g * 150 + b * 29) / 256
} else {
gray[i] = img.data[i * ch].to_int()
}
}
// 内部区域：x in [1, w-1), y in [1, h-1)，直接访问 4 邻域
for y in 1..<(h - 1) {
for x in 1..<(w - 1) {
let idx = y * w + x
let center = gray[idx]
let u = gray[idx - w]
let d = gray[idx + w]
let l = gray[idx - 1]
let r = gray[idx + 1]
let lap = u + d + l + r - 4 * center
out[idx] = clamp_b(lap.abs())
}
}
// 边界区域
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 cx = clamp_i(x, 0, w - 1)
let cy = clamp_i(y, 0, h - 1)
let up = clamp_i(y - 1, 0, h - 1)
let down = clamp_i(y + 1, 0, h - 1)
let left = clamp_i(x - 1, 0, w - 1)
let right = clamp_i(x + 1, 0, w - 1)
let center = gray[cy * w + cx]
let u = gray[up * w + cx]
let d = gray[down * w + cx]
let l = gray[cy * w + left]
let r = gray[cy * w + right]
let lap = u + d + l + r - 4 * center
out[y * w + x] = clamp_b(lap.abs())
}
}
@types.Image::{
width: w,
height: h,
channels: 1,
data: Bytes::from_array(out),
}
}
///|
/// Prewitt 边缘检测。使用两个 3x3 Prewitt 核计算梯度幅值。
/// Gx = [-1, 0, 1; -1, 0, 1; -1, 0, 1]
/// Gy = [-1, -1, -1; 0, 0, 0; 1, 1, 1]
/// 返回灰度图（channels=1）。
pub fn edge_detect_prewitt(
img : @types.Image,
) -> @types.Image raise @types.LoadError {
// 输入验证
if img.width <= 0 || img.height <= 0 {
raise @types.LoadError::DecodeFailed(
"edge_detect_prewitt: 图像尺寸必须大于0",
)
}
if img.channels < 1 || img.channels > 4 {
raise @types.LoadError::DecodeFailed(
"edge_detect_prewitt: 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(
"edge_detect_prewitt: 数据大小不匹配，期望 " +
expected_size.to_string() +
"，实际 " +
img.data.length().to_string(),
)
}
let ch = img.channels
let w = img.width
let h = img.height
let n = w * h
let out : Array[Byte] = Array::make(n, b'\x00')
// 预计算灰度 Int 数组
let gray : Array[Int] = Array::make(n, 0)
for i in 0..<n {
if ch >= 3 {
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()
gray[i] = (r * 77 + g * 150 + b * 29) / 256
} else {
gray[i] = img.data[i * ch].to_int()
}
}
// 内部区域：x in [1, w-1), y in [1, h-1)
for y in 1..<(h - 1) {
for x in 1..<(w - 1) {
let idx = y * w + x
let ul = gray[idx - w - 1]
let ur = gray[idx - w + 1]
let ll = gray[idx + w - 1]
let lr = gray[idx + w + 1]
let l = gray[idx - 1]
let r = gray[idx + 1]
let u = gray[idx - w]
let d = gray[idx + w]
let gx = ur + r + lr - ul - l - ll
let gy = ll + d + lr - ul - u - ur
let mag = Float::from_int(gx * gx + gy * gy).sqrt()
out[idx] = clamp_b(mag.to_int())
}
}
// 边界区域
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 up = clamp_i(y - 1, 0, h - 1)
let down = clamp_i(y + 1, 0, h - 1)
let left = clamp_i(x - 1, 0, w - 1)
let right = clamp_i(x + 1, 0, w - 1)
let ul = gray[up * w + left]
let ur = gray[up * w + right]
let ll = gray[down * w + left]
let lr = gray[down * w + right]
let l = gray[y * w + left]
let r = gray[y * w + right]
let u = gray[up * w + x]
let d = gray[down * w + x]
let gx = ur + r + lr - ul - l - ll
let gy = ll + d + lr - ul - u - ur
let mag = Float::from_int(gx * gx + gy * gy).sqrt()
out[y * w + x] = clamp_b(mag.to_int())
}
}
@types.Image::{
width: w,
height: h,
channels: 1,
data: Bytes::from_array(out),
}
}