convolve-nd 0.1.0

A small library to perform convolution operations on arrays of upto 3 dimensions using arbitrarily-sized separable kernels
Documentation 
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

use ndarray::{Array1, Array2, Array3};
use crate::Convolution;
use crate::kernel::SeparableKernel;
use crate::dimensions::DimensionIterator;

impl Convolution for Array1<f32> {
    fn convolve<const KERNEL_SIZE: usize>(&mut self, kernel: SeparableKernel<KERNEL_SIZE>, stride: usize) {
        let linear_kernel = kernel.values();

        let sample_length = self.len();
        
        for index in 0..sample_length {
            let mut signal_sum = 0.;

            for (kernel_index, value) in linear_kernel.iter().enumerate() {
                let relative_kernel_index = kernel_index as isize - (KERNEL_SIZE as isize / 2);
                let signal_index = Self::compute_signal_index(
                    stride,
                    KERNEL_SIZE,
                    relative_kernel_index,
                    index,
                    sample_length
                );

                signal_sum += self[signal_index as usize] * *value;
            }

            self[index] = signal_sum;
        }
    }
}

impl Convolution for Array2<f32> {
    fn convolve<const KERNEL_SIZE: usize>(&mut self, kernel: SeparableKernel<KERNEL_SIZE>, stride: usize) {
        let linear_kernel = kernel.values();

        let (height, width) = self.dim();
        let dimensions = self.raw_dim();

        for (y, x) in dimensions.into_iter() {
            let mut signal_sum = 0.;

            for (kernel_index, value) in linear_kernel.iter().enumerate() {
                let relative_kernel_index = kernel_index as isize - (KERNEL_SIZE as isize / 2);
                let signal_index = Self::compute_signal_index(
                    stride,
                    KERNEL_SIZE,
                    relative_kernel_index,
                    x,
                    width
                );

                signal_sum += self[[y, signal_index as usize]] * *value;
            }

            self[[y, x]] = signal_sum;
        }

        for (y, x) in dimensions.into_iter() {
            let mut signal_sum = 0.;

            for (kernel_index, value) in linear_kernel.iter().enumerate() {
                let relative_kernel_index = kernel_index as isize - (KERNEL_SIZE as isize / 2);
                let signal_index = Self::compute_signal_index(
                    stride,
                    KERNEL_SIZE,
                    relative_kernel_index,
                    y,
                    height
                );

                signal_sum += self[[signal_index as usize, x]] * *value;
            }

            self[[y, x]] = signal_sum;
        }
    }
}

impl Convolution for Array3<f32> {
    fn convolve<const KERNEL_SIZE: usize>(&mut self, kernel: SeparableKernel<KERNEL_SIZE>, stride: usize) {
        let linear_kernel = kernel.values();
        let (height, width, _) = self.dim();
        let dimensions = self.raw_dim();

        for (y, x, z) in dimensions.into_iter() {
            let mut signal_sum = 0.;

            for (kernel_index, value) in linear_kernel.iter().enumerate() {
                let relative_kernel_index = kernel_index as isize - (KERNEL_SIZE as isize / 2);
                let signal_index = Self::compute_signal_index(
                    stride,
                    KERNEL_SIZE,
                    relative_kernel_index,
                    x,
                    width
                );

                signal_sum += self[[y, signal_index as usize, z]] * *value;
            }

            self[[y, x, z]] = signal_sum;
        }

        for (y, x, z) in dimensions.into_iter() {
            let mut signal_sum = 0.;

            for (kernel_index, value) in linear_kernel.iter().enumerate() {
                let relative_kernel_index = kernel_index as isize - (KERNEL_SIZE as isize / 2);
                let signal_index = Self::compute_signal_index(
                    stride,
                    KERNEL_SIZE,
                    relative_kernel_index,
                    y,
                    height
                );

                signal_sum += self[[signal_index as usize, x, z]] * *value;
            }

            self[[y, x, z]] = signal_sum;
        }
    }
}

pub(crate) trait Aggregate {
    fn min(&self) -> f32;
    fn max(&self) -> f32;
}

macro_rules! impl_aggregate {
    ($ty:ty) => {
        impl Aggregate for $ty {
            fn min(&self) -> f32 {
                *self.iter()
                    .reduce(|current, previous| {
                        if current < previous {
                            current
                        } else {
                            previous
                        }
                    })
                    .unwrap()
            }

            fn max(&self) -> f32 {
                *self
                    .iter()
                    .reduce(|current, previous| {
                        if current > previous {
                            current
                        } else {
                            previous
                        }
                    })
                    .unwrap()
            }
        }
    };
}

impl_aggregate!(Array1<f32>);
impl_aggregate!(Array2<f32>);
impl_aggregate!(Array3<f32>);