use crate::morphology::impl_generic::{
black_tophat_impl, grey_closing_impl, grey_dilation_impl, grey_erosion_impl, grey_opening_impl,
morphological_gradient_impl, white_tophat_impl,
};
use crate::morphology::traits::grey::GreyMorphologyAlgorithms;
use numr::error::Result;
use numr::runtime::cpu::{CpuClient, CpuRuntime};
use numr::tensor::Tensor;
impl GreyMorphologyAlgorithms<CpuRuntime> for CpuClient {
fn grey_erosion(
&self,
input: &Tensor<CpuRuntime>,
size: &[usize],
) -> Result<Tensor<CpuRuntime>> {
grey_erosion_impl(self, input, size)
}
fn grey_dilation(
&self,
input: &Tensor<CpuRuntime>,
size: &[usize],
) -> Result<Tensor<CpuRuntime>> {
grey_dilation_impl(self, input, size)
}
fn grey_opening(
&self,
input: &Tensor<CpuRuntime>,
size: &[usize],
) -> Result<Tensor<CpuRuntime>> {
grey_opening_impl(self, input, size)
}
fn grey_closing(
&self,
input: &Tensor<CpuRuntime>,
size: &[usize],
) -> Result<Tensor<CpuRuntime>> {
grey_closing_impl(self, input, size)
}
fn morphological_gradient(
&self,
input: &Tensor<CpuRuntime>,
size: &[usize],
) -> Result<Tensor<CpuRuntime>> {
morphological_gradient_impl(self, input, size)
}
fn white_tophat(
&self,
input: &Tensor<CpuRuntime>,
size: &[usize],
) -> Result<Tensor<CpuRuntime>> {
white_tophat_impl(self, input, size)
}
fn black_tophat(
&self,
input: &Tensor<CpuRuntime>,
size: &[usize],
) -> Result<Tensor<CpuRuntime>> {
black_tophat_impl(self, input, size)
}
}
#[cfg(test)]
mod tests {
use super::*;
use numr::runtime::cpu::CpuDevice;
fn setup() -> (CpuClient, CpuDevice) {
let device = CpuDevice::new();
let client = CpuClient::new(device.clone());
(client, device)
}
#[test]
fn test_grey_erosion() {
let (client, device) = setup();
let data = vec![5.0, 3.0, 7.0, 1.0, 4.0];
let input = Tensor::from_slice(&data, &[5], &device);
let result = client.grey_erosion(&input, &[3]).unwrap();
let out: Vec<f64> = result.to_vec();
assert!((out[2] - 1.0).abs() < 1e-10);
}
#[test]
fn test_morphological_gradient() {
let (client, device) = setup();
let data = vec![0.0, 0.0, 5.0, 5.0, 5.0];
let input = Tensor::from_slice(&data, &[5], &device);
let result = client.morphological_gradient(&input, &[3]).unwrap();
let out: Vec<f64> = result.to_vec();
assert!(out[1] > 0.0 || out[2] > 0.0);
}
}