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
use std::f64;
use std::ops;
use crate::*;
#[derive(Debug, Clone, PartialEq)]
#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
pub struct Kernel {
rows: usize,
cols: usize,
data: Vec<Vec<f64>>,
}
impl From<Vec<Vec<f64>>> for Kernel {
fn from(data: Vec<Vec<f64>>) -> Kernel {
let rows = data.len();
let cols = data[0].len();
Kernel { data, rows, cols }
}
}
impl<'a> From<&'a [&'a [f64]]> for Kernel {
fn from(data: &'a [&'a [f64]]) -> Kernel {
let rows = data.len();
let cols = data[0].len();
let mut v = Vec::new();
for d in data {
v.push(Vec::from(*d))
}
Kernel {
data: v,
rows,
cols,
}
}
}
impl<const N: usize> From<[[f64; N]; N]> for Kernel {
fn from(data: [[f64; N]; N]) -> Kernel {
let data = data.iter().map(|d| d.to_vec()).collect();
Kernel {
data,
rows: N,
cols: N,
}
}
}
impl<T: Type, C: Color, U: Type, D: Color> Filter<T, C, U, D> for Kernel {
fn schedule(&self) -> Schedule {
Schedule::Image
}
fn compute_at(&self, pt: Point, input: &Input<T, C>, dest: &mut DataMut<U, D>) {
let r2 = (self.rows / 2) as isize;
let c2 = (self.cols / 2) as isize;
let mut f = input.new_pixel();
let mut x: f64;
for ky in -r2..=r2 {
let kr = &self.data[(ky + r2) as usize];
let pty = (pt.y as isize + ky) as usize;
for kx in -c2..=c2 {
let krc = kr[(kx + c2) as usize];
for c in 0..f.len() {
x = input.get_f(((pt.x as isize + kx) as usize, pty), c, Some(0));
f[c] += x * krc;
}
}
}
f.copy_to_slice(dest);
}
}
impl Kernel {
pub fn new(rows: usize, cols: usize) -> Kernel {
let data = vec![vec![0.0; cols]; rows];
Kernel { data, rows, cols }
}
pub fn square(x: usize) -> Kernel {
Self::new(x, x)
}
pub fn normalize(&mut self) {
let sum: f64 = self.data.iter().map(|x| -> f64 { x.iter().sum() }).sum();
if sum == 0.0 {
return;
}
for j in 0..self.rows {
for i in 0..self.cols {
self.data[j][i] /= sum
}
}
}
pub fn create<F: Fn(usize, usize) -> f64>(rows: usize, cols: usize, f: F) -> Kernel {
let mut k = Self::new(rows, cols);
for j in 0..rows {
let d = &mut k.data[j];
for (i, item) in d.iter_mut().enumerate() {
*item = f(i, j);
}
}
k
}
pub fn gaussian(n: usize, std: f64) -> Kernel {
assert!(n % 2 != 0);
let std2 = std * std;
let a = 1.0 / (2.0 * f64::consts::PI * std2);
let mut k = Kernel::create(n, n, |i, j| {
let x = (i * i + j * j) as f64 / (2.0 * std2);
a * f64::consts::E.powf(-1.0 * x)
});
k.normalize();
k
}
pub fn gaussian_3x3() -> Kernel {
Self::gaussian(3, 1.4)
}
pub fn gaussian_5x5() -> Kernel {
Self::gaussian(5, 1.4)
}
pub fn gaussian_7x7() -> Kernel {
Self::gaussian(7, 1.4)
}
pub fn gaussian_9x9() -> Kernel {
Self::gaussian(9, 1.4)
}
pub fn sobel_x() -> Kernel {
Kernel {
rows: 3,
cols: 3,
data: vec![
vec![1.0, 0.0, -1.0],
vec![2.0, 0.0, -2.0],
vec![1.0, 0.0, -1.0],
],
}
}
pub fn sobel_y() -> Kernel {
Kernel {
rows: 3,
cols: 3,
data: vec![
vec![1.0, 2.0, 1.0],
vec![0.0, 0.0, 0.0],
vec![-1.0, -2.0, -1.0],
],
}
}
pub fn laplacian() -> Kernel {
Kernel::from([[0., -1., 0.], [-1., 4., -1.], [0., -1., 0.]])
}
pub fn sobel() -> Kernel {
Kernel::sobel_x() + Kernel::sobel_y()
}
}
impl ops::Add for Kernel {
type Output = Kernel;
fn add(mut self, other: Kernel) -> Kernel {
for i in 0..self.rows {
for j in 0..self.cols {
self.data[i][j] += other.data[i][j];
}
}
self
}
}
impl ops::Sub for Kernel {
type Output = Kernel;
fn sub(mut self, other: Kernel) -> Kernel {
for i in 0..self.rows {
for j in 0..self.cols {
self.data[i][j] -= other.data[i][j];
}
}
self
}
}
impl ops::Mul for Kernel {
type Output = Kernel;
fn mul(mut self, other: Kernel) -> Kernel {
for i in 0..self.rows {
for j in 0..self.cols {
self.data[i][j] *= other.data[i][j];
}
}
self
}
}
impl ops::Div for Kernel {
type Output = Kernel;
fn div(mut self, other: Kernel) -> Kernel {
for i in 0..self.rows {
for j in 0..self.cols {
self.data[i][j] /= other.data[i][j];
}
}
self
}
}