convolve-image 0.4.0

A small library to perform convolution operations on images with arbitrarily-sized separable kernels
Documentation 
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#![cfg(feature = "ndarray")]

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

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 pixel_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 pixel_index = Self::compute_pixel_index(
                    stride,
                    KERNEL_SIZE,
                    relative_kernel_index,
                    x,
                    width
                );

                pixel_sum += self[[y, pixel_index as usize]] * *value;
            }

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

        for (y, x) in dimensions.into_iter() {
            let mut pixel_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 pixel_index = Self::compute_pixel_index(
                    stride,
                    KERNEL_SIZE,
                    relative_kernel_index,
                    y,
                    height
                );

                pixel_sum += self[[pixel_index as usize, x]] * *value;
            }

            self[[y, x]] = pixel_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, channel) in dimensions.into_iter() {
            let mut pixel_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 pixel_index = Self::compute_pixel_index(
                    stride,
                    KERNEL_SIZE,
                    relative_kernel_index,
                    x,
                    width
                );

                pixel_sum += self[[y, pixel_index as usize, channel]] * *value;
            }

            self[[y, x, channel]] = pixel_sum;   
        }

        for (y, x, channel) in dimensions.into_iter() {
            let mut pixel_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 pixel_index = Self::compute_pixel_index(
                    stride,
                    KERNEL_SIZE,
                    relative_kernel_index,
                    y,
                    height
                );

                pixel_sum += self[[pixel_index as usize, x, channel]] * *value;
            }

            self[[y, x, channel]] = pixel_sum;
        }
    }
}

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

impl Aggregate for Array2<f32> {
    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 for Array3<f32> {
    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()
    }
}