Now build SimpleITK into static libs and use (auto)cxx.
This commit is contained in:
Wim Pomp
@@ -0,0 +1,645 @@
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//! Some structs and methods to make working with registration and interpolation methods in
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//! SimpleITK more Rust friendly.
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use crate::simple;
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use anyhow::{Result, anyhow};
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use autocxx::prelude::*;
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use cxx::{CxxVector, UniquePtr, let_cxx_string};
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use ndarray::{Array2, ArrayView2, AsArray, Ix2, array, s};
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use serde::{Deserialize, Serialize};
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use serde_yaml::{from_reader, to_writer};
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use std::fs::File;
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use std::marker::PhantomData;
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use std::ops::{Deref, DerefMut, Mul};
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use std::path::PathBuf;
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use num::Complex;
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use tempfile::tempdir;
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/// a trait marking number types that can be used in sitk:
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/// (u/i)(8/16/32/64), (u/i)size, f(32/64)
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pub trait PixelType: Clone {
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const PT: simple::PixelIDValueEnum;
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}
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macro_rules! pixel_type_impl {
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($($T:ty: $sitk:expr $(,)?)*) => {
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$(
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impl PixelType for $T {
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const PT: simple::PixelIDValueEnum = $sitk;
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}
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)*
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};
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}
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pixel_type_impl! {
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u8: simple::PixelIDValueEnum::sitkUInt8,
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i8: simple::PixelIDValueEnum::sitkInt8,
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u16: simple::PixelIDValueEnum::sitkUInt16,
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i16: simple::PixelIDValueEnum::sitkInt16,
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u32: simple::PixelIDValueEnum::sitkUInt32,
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i32: simple::PixelIDValueEnum::sitkInt32,
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u64: simple::PixelIDValueEnum::sitkUInt64,
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i64: simple::PixelIDValueEnum::sitkInt64,
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f32: simple::PixelIDValueEnum::sitkFloat32,
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f64: simple::PixelIDValueEnum::sitkFloat64,
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}
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#[cfg(target_pointer_width = "64")]
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pixel_type_impl!(usize: simple::PixelIDValueEnum::sitkUInt64);
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#[cfg(target_pointer_width = "32")]
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pixel_type_impl!(usize: simple::PixelIDValueEnum::sitkUInt32);
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#[cfg(target_pointer_width = "64")]
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pixel_type_impl!(isize: simple::PixelIDValueEnum::sitkInt64);
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#[cfg(target_pointer_width = "32")]
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pixel_type_impl!(isize: simple::PixelIDValueEnum::sitkInt32);
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/// Struct holding a pointer to an image
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pub struct Image<T: PixelType> {
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image: UniquePtr<simple::Image>,
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pixel_type: PhantomData<T>,
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}
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impl<T: PixelType> Deref for Image<T> {
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type Target = UniquePtr<simple::Image>;
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fn deref(&self) -> &Self::Target {
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&self.image
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}
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}
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impl<T: PixelType> Image<T> {
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/// encapsulate an itk::simple::Image
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pub fn new(image: UniquePtr<simple::Image>) -> Self {
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Self {
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image,
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pixel_type: PhantomData,
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}
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}
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/// take an ndarray Array2 and turn it into a SimpleITK image
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pub fn from_array<'a, A>(array: A) -> Self
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where
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T: 'a + PixelType,
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A: AsArray<'a, T, Ix2>,
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{
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let array = array.into();
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let shape = array.shape();
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let width = (shape[1] as u32).into();
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let height = (shape[0] as u32).into();
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let mut image = simple::Image::new3(width, height, T::PT).within_unique_ptr();
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image.pin_mut().MakeUnique();
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let buffer = image.pin_mut().GetBufferAsVoid();
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unsafe { std::ptr::copy(array.as_ptr(), buffer as *mut T, shape[0] * shape[1]) };
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Self {
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image,
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pixel_type: PhantomData,
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}
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}
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/// return as an ndarray Array2
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pub fn as_array(&self) -> Array2<T> {
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let width = u32::from(self.image.GetWidth()) as usize;
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let height = u32::from(self.image.GetHeight()) as usize;
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let mut array = Array2::<T>::uninit((height, width));
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let buffer = self.image.GetBufferAsVoid1();
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unsafe {
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std::ptr::copy(
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buffer as *const T,
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array.as_mut_ptr() as *mut T,
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width * height,
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);
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array.assume_init()
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}
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}
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}
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impl<'a, A, T> From<A> for Image<T>
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where
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T: 'a + PixelType,
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A: AsArray<'a, T, Ix2>,
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{
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fn from(value: A) -> Self {
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Self::from_array(value.into())
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}
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}
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impl<T> From<Image<T>> for Array2<T>
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where
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T: PixelType,
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{
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fn from(value: Image<T>) -> Self {
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value.as_array()
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}
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}
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/// a struct describing the transform
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#[derive(Clone, Debug, Deserialize, Serialize, PartialEq)]
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pub struct AffineTransform {
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/// flattened 2x2 rotation matrix + translation
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pub parameters: [f64; 6],
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/// error / significance on parameters
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pub dparameters: [f64; 6],
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/// the point about which rotations are performed
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pub origin: [f64; 2],
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/// the shape of images for which this transform is meant
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pub shape: [usize; 2],
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}
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impl Mul for AffineTransform {
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type Output = AffineTransform;
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#[allow(clippy::suspicious_arithmetic_impl)]
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fn mul(self, other: AffineTransform) -> AffineTransform {
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let m = self.matrix().dot(&other.matrix());
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let dm = self.dmatrix().dot(&other.matrix()) + self.matrix().dot(&other.dmatrix());
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AffineTransform {
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parameters: [
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m[[0, 0]],
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m[[0, 1]],
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m[[1, 0]],
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m[[1, 1]],
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m[[2, 0]],
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m[[2, 1]],
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],
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dparameters: [
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dm[[0, 0]],
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dm[[0, 1]],
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dm[[1, 0]],
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dm[[1, 1]],
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dm[[2, 0]],
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dm[[2, 1]],
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],
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origin: self.origin,
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shape: self.shape,
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}
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}
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}
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impl Eq for AffineTransform {}
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impl AffineTransform {
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/// parameters: flat 2x2 part of matrix, translation; origin: center of rotation
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pub fn new(parameters: [f64; 6], origin: [f64; 2], shape: [usize; 2]) -> Self {
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Self {
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parameters,
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dparameters: [0f64; 6],
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origin,
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shape,
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}
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}
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/// find the affine transform which transforms moving into fixed
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pub fn register_affine<F, M, T>(fixed: F, moving: M) -> Result<AffineTransform>
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where
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F: Into<Image<T>>,
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M: Into<Image<T>>,
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T: PixelType,
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{
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Self::register(fixed, moving, true)
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}
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/// find the translation which transforms moving into fixed
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pub fn register_translation<F, M, T>(fixed: F, moving: M) -> Result<AffineTransform>
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where
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F: Into<Image<T>>,
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M: Into<Image<T>>,
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T: PixelType,
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{
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Self::register(fixed, moving, false)
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}
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/// find the transform which transforms moving into fixed
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pub fn register<F, M, T>(fixed: F, moving: M, affine: bool) -> Result<AffineTransform>
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where
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F: Into<Image<T>>,
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M: Into<Image<T>>,
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T: PixelType,
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{
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let tmp_folder = tempdir()?;
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let fixed = fixed.into();
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let moving = moving.into();
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let width = u32::from(fixed.GetWidth()) as usize;
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let height = u32::from(fixed.GetHeight()) as usize;
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let_cxx_string!(transform_name = if affine { "affine" } else { "translation" });
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let parameter_map = crate::ffi_extra::get_default_parameter_map(&transform_name);
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let mut tfilter = simple::ElastixImageFilter::new().within_box();
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tfilter.as_mut().LogToConsoleOff();
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tfilter.as_mut().LogToFileOff();
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tfilter.as_mut().SetLogToFile(false);
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tfilter.as_mut().SetFixedImage(&fixed);
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tfilter.as_mut().SetMovingImage(&moving);
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crate::ffi_extra::set_parameter_map(&mut tfilter, ¶meter_map);
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tfilter.as_mut().SetParameter("WriteResultImage", "False");
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tfilter
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.as_mut()
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.SetOutputDirectory(tmp_folder.path().display().to_string());
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let _ = tfilter.as_mut().Execute().within_unique_ptr();
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let_cxx_string!(tp = "TransformParameters");
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let p = crate::ffi_extra::get_transform_parameter_map(tfilter.as_mut().deref_mut(), 0)
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.get(&tp)
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.iter()
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.map(|i| i.to_string_lossy().parse::<f64>())
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.collect::<Result<Vec<f64>, _>>()?;
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let parameters = if affine {
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[p[0], p[1], p[2], p[3], p[4], p[5]]
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} else {
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[1.0, 0.0, 0.0, 1.0, p[0], p[1]]
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};
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let origin = [((height - 1) as f64) / 2.0, ((width - 1) as f64) / 2.0];
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let shape = [height, width];
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Ok(AffineTransform::new(parameters, origin, shape))
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}
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/// create a transform from a xy translation
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pub fn from_translation(translation: [f64; 2]) -> Self {
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AffineTransform {
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parameters: [1f64, 0f64, 0f64, 1f64, translation[0], translation[1]],
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dparameters: [0f64; 6],
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origin: [0f64; 2],
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shape: [0usize; 2],
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}
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}
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/// read a transform from a file
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pub fn from_file(path: PathBuf) -> Result<Self> {
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let file = File::open(path)?;
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Ok(from_reader(file)?)
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}
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/// write a transform to a file
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pub fn to_file(&self, path: PathBuf) -> Result<()> {
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let mut file = std::fs::OpenOptions::new()
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.create(true)
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.write(true)
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.truncate(true)
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.open(path)?;
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to_writer(&mut file, self)?;
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Ok(())
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}
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/// true if transform does nothing
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pub fn is_unity(&self) -> bool {
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self.parameters == [1f64, 0f64, 0f64, 1f64, 0f64, 0f64]
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}
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/// transform an image using nearest neighbor interpolation
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pub fn transform_image_bspline<I, T>(&self, image: I) -> Result<Image<T>>
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where
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I: Into<Image<T>>,
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T: PixelType,
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{
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Self::interp(self, image, false)
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}
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/// transform an image using bspline interpolation
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pub fn transform_image_nearest_neighbor<I, T>(&self, image: I) -> Result<Image<T>>
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where
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I: Into<Image<T>>,
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T: PixelType,
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{
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Self::interp(self, image, true)
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}
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/// transform an image
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pub fn interp<I, T>(&self, image: I, nearest_neighbor: bool) -> Result<Image<T>>
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where
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I: Into<Image<T>>,
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T: PixelType,
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{
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let image = image.into();
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let width = u32::from(image.GetWidth()) as usize;
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let height = u32::from(image.GetHeight()) as usize;
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let origin = [((width - 1) as f64) / 2f64, ((height - 1) as f64) / 2f64];
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let p = self.parameters;
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let matrix = cxx_vector([p[0], p[1], p[2], p[3]]);
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let translation = cxx_vector([p[4], p[5]]);
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let fixed_center = cxx_vector(origin);
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let affine_transform =
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simple::AffineTransform::new3(&matrix, &translation, &fixed_center).within_unique_ptr();
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let transform = <_ as AsRef<simple::Transform>>::as_ref(affine_transform.as_ref().unwrap());
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let interpolator = if nearest_neighbor {
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simple::InterpolatorEnum::sitkBSpline
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} else {
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simple::InterpolatorEnum::sitkNearestNeighbor
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};
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Ok(Image::<T>::new(
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simple::Resample(
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&image,
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transform,
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interpolator,
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0.0,
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simple::PixelIDValueEnum::sitkUnknown,
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nearest_neighbor,
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)
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.within_unique_ptr(),
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))
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}
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/// get coordinates resulting from transforming input coordinates, coordinates must have two
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/// columns: x & y
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pub fn transform_coordinates<'a, A, T>(&self, coordinates: A) -> Result<Array2<f64>>
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where
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T: 'a + Clone + Into<f64>,
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A: AsArray<'a, T, Ix2>,
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{
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let coordinates = coordinates.into();
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let s = coordinates.shape();
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if s[1] != 2 {
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return Err(anyhow!("coordinates must have two columns"));
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}
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let m = self.matrix();
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let mut res = Array2::zeros([s[0], s[1]]);
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for i in 0..s[0] {
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let a = array![
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coordinates[[i, 0]].clone().into(),
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coordinates[[i, 1]].clone().into(),
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1f64
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]
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.to_owned();
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let b = m.dot(&a);
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res.slice_mut(s![i, ..]).assign(&b.slice(s![..2]));
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}
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Ok(res)
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}
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/// get the matrix defining the transform
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pub fn matrix(&self) -> Array2<f64> {
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Array2::from_shape_vec(
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(3, 3),
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vec![
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self.parameters[0],
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self.parameters[1],
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self.parameters[4],
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self.parameters[2],
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self.parameters[3],
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self.parameters[5],
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0f64,
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0f64,
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1f64,
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],
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)
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.unwrap()
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}
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/// get the matrix describing the error of the transform
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pub fn dmatrix(&self) -> Array2<f64> {
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Array2::from_shape_vec(
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(3, 3),
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vec![
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self.dparameters[0],
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self.dparameters[1],
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self.dparameters[4],
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self.dparameters[2],
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self.dparameters[3],
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self.dparameters[5],
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0f64,
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0f64,
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1f64,
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],
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)
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.unwrap()
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}
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/// get the inverse transform
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pub fn inverse(&self) -> Result<AffineTransform> {
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fn det(a: ArrayView2<f64>) -> f64 {
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(a[[0, 0]] * a[[1, 1]]) - (a[[0, 1]] * a[[1, 0]])
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}
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let m = self.matrix();
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let d = det(m.slice(s![..2, ..2]));
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if d == 0f64 {
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return Err(anyhow!("transform matrix is not invertible"));
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}
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let parameters = [
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det(m.slice(s![1.., 1..])) / d,
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-det(m.slice(s![..;2, 1..])) / d,
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-det(m.slice(s![1.., ..;2])) / d,
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det(m.slice(s![..;2, ..;2])) / d,
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det(m.slice(s![..2, 1..])) / d,
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-det(m.slice(s![..2, ..;2])) / d,
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];
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Ok(AffineTransform {
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parameters,
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dparameters: [0f64; 6],
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origin: self.origin,
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shape: self.shape,
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})
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}
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/// adapt the transform to a new origin and shape
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pub fn adapt(&mut self, origin: [f64; 2], shape: [usize; 2]) {
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self.origin = [
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origin[0] + (((self.shape[0] - shape[0]) as f64) / 2f64),
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origin[1] + (((self.shape[1] - shape[1]) as f64) / 2f64),
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];
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self.shape = shape;
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}
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}
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/// conveniently collect an iterator into a CxxVector
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pub fn cxx_vector<T, I>(vec: I) -> UniquePtr<CxxVector<T>>
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where
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I: IntoIterator<Item = T>,
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T: cxx::vector::VectorElement + cxx::ExternType<Kind = cxx::kind::Trivial>,
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{
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let mut v = CxxVector::new();
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v.pin_mut().extend(vec);
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v
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}
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/// An example of generating julia fractals, for testing purposes.
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pub fn julia_image(shift_x: f32, shift_y: f32) -> Result<Array2<u8>> {
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let imgx = 800;
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let imgy = 600;
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let scalex = 3.0 / imgx as f32;
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let scaley = 3.0 / imgy as f32;
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let mut im = Array2::<u8>::zeros((imgy, imgx));
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for x in 0..imgx {
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for y in 0..imgy {
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let cy = (y as f32 + shift_y) * scalex - 1.5;
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let cx = (x as f32 + shift_x) * scaley - 1.5;
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let c = Complex::new(-0.4, 0.6);
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let mut z = Complex::new(cy, cx);
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let mut i = 0;
|
||||
while i < 255 && z.norm() <= 2.0 {
|
||||
z = z * z + c;
|
||||
i += 1;
|
||||
}
|
||||
|
||||
im[[y, x]] = i as u8;
|
||||
}
|
||||
}
|
||||
Ok(im)
|
||||
}
|
||||
|
||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use anyhow::Result;
|
||||
use ndarray::Array2;
|
||||
use tempfile::NamedTempFile;
|
||||
|
||||
#[test]
|
||||
fn test_serialization() -> Result<()> {
|
||||
let file = NamedTempFile::new()?;
|
||||
let t = AffineTransform::new([1.2, 0.3, -0.4, 0.9, 10.2, -9.5], [59.5, 49.5], [120, 100]);
|
||||
t.to_file(file.path().to_path_buf())?;
|
||||
let s = AffineTransform::from_file(file.path().to_path_buf())?;
|
||||
assert_eq!(s, t);
|
||||
Ok(())
|
||||
}
|
||||
|
||||
macro_rules! interp_tests_bspline {
|
||||
($($name:ident: $t:ty $(,)?)*) => {
|
||||
$(
|
||||
#[test]
|
||||
fn $name() -> Result<()> {
|
||||
let j = julia_image(-120f32, 10f32)?.mapv(|x| x as $t);
|
||||
let k = julia_image(0f32, 0f32)?.mapv(|x| x as $t);
|
||||
let shape = j.shape();
|
||||
let origin = [
|
||||
((shape[1] - 1) as f64) / 2f64,
|
||||
((shape[0] - 1) as f64) / 2f64,
|
||||
];
|
||||
let transform = AffineTransform::new([1., 0., 0., 1., 120., -10.], origin, [shape[0], shape[1]]);
|
||||
let n: Array2<_> = transform.transform_image_bspline(j.view())?.into();
|
||||
let d = (k.mapv(|x| x as f64) - n.mapv(|x| x as f64)).powi(2).sum();
|
||||
assert!(d <= (shape[0] * shape[1]) as f64);
|
||||
Ok(())
|
||||
}
|
||||
)*
|
||||
}
|
||||
}
|
||||
|
||||
interp_tests_bspline! {
|
||||
interpbs_u8: u8,
|
||||
interpbs_i8: i8,
|
||||
interpbs_u16: u16,
|
||||
interpbs_i16: i16,
|
||||
interpbs_u32: u32,
|
||||
interpbs_i32: i32,
|
||||
interpbs_u64: u64,
|
||||
interpbs_i64: i64,
|
||||
interpbs_f32: f32,
|
||||
interpbs_f64: f64,
|
||||
}
|
||||
|
||||
macro_rules! interp_tests_nearest_neighbor {
|
||||
($($name:ident: $t:ty $(,)?)*) => {
|
||||
$(
|
||||
#[test]
|
||||
fn $name() -> Result<()> {
|
||||
let j = julia_image(-120f32, 10f32)?.mapv(|x| x as $t);
|
||||
let k = julia_image(0f32, 0f32)?.mapv(|x| x as $t);
|
||||
let shape = j.shape();
|
||||
let origin = [
|
||||
((shape[1] - 1) as f64) / 2f64,
|
||||
((shape[0] - 1) as f64) / 2f64,
|
||||
];
|
||||
let j0 = j.clone();
|
||||
let k0 = k.clone();
|
||||
let transform = AffineTransform::new([1., 0., 0., 1., 120., -10.], origin, [shape[0], shape[1]]);
|
||||
// make sure j & k weren't mutated
|
||||
assert!(j.iter().zip(j0.iter()).map(|(a, b)| a == b).all(|x| x));
|
||||
assert!(k.iter().zip(k0.iter()).map(|(a, b)| a == b).all(|x| x));
|
||||
let n: Array2<_> = transform.transform_image_nearest_neighbor(j.view())?.into();
|
||||
let d = (k.mapv(|x| x as f64) - n.mapv(|x| x as f64)).powi(2).sum();
|
||||
assert!(d <= (shape[0] * shape[1]) as f64);
|
||||
Ok(())
|
||||
}
|
||||
)*
|
||||
}
|
||||
}
|
||||
|
||||
interp_tests_nearest_neighbor! {
|
||||
interpnn_u8: u8,
|
||||
interpnn_i8: i8,
|
||||
interpnn_u16: u16,
|
||||
interpnn_i16: i16,
|
||||
interpnn_u32: u32,
|
||||
interpnn_i32: i32,
|
||||
interpnn_u64: u64,
|
||||
interpnn_i64: i64,
|
||||
interpnn_f32: f32,
|
||||
interpnn_f64: f64,
|
||||
}
|
||||
|
||||
macro_rules! registration_tests_translation {
|
||||
($($name:ident: $t:ty $(,)?)*) => {
|
||||
$(
|
||||
#[test]
|
||||
fn $name() -> Result<()> {
|
||||
let j = julia_image(0f32, 0f32)?.mapv(|x| x as $t);
|
||||
let k = julia_image(10f32, 20f32)?.mapv(|x| x as $t);
|
||||
let j0 = j.clone();
|
||||
let k0 = k.clone();
|
||||
let t = AffineTransform::register_translation(j.view(), k.view())?;
|
||||
// make sure j & k weren't mutated
|
||||
assert!(j.iter().zip(j0.iter()).map(|(a, b)| a == b).all(|x| x));
|
||||
assert!(k.iter().zip(k0.iter()).map(|(a, b)| a == b).all(|x| x));
|
||||
let mut m = Array2::eye(3);
|
||||
m[[0, 2]] = -10f64;
|
||||
m[[1, 2]] = -20f64;
|
||||
let d = (t.matrix() - m).powi(2).sum();
|
||||
assert!(d < 0.01, "d: {}, t: {:?}", d, t.parameters);
|
||||
Ok(())
|
||||
}
|
||||
)*
|
||||
}
|
||||
}
|
||||
|
||||
registration_tests_translation! {
|
||||
registration_translation_u8: u8,
|
||||
registration_translation_i8: i8,
|
||||
registration_translation_u16: u16,
|
||||
registration_translation_i16: i16,
|
||||
registration_translation_u32: u32,
|
||||
registration_translation_i32: i32,
|
||||
registration_translation_u64: u64,
|
||||
registration_translation_i64: i64,
|
||||
registration_translation_f32: f32,
|
||||
registration_translation_f64: f64,
|
||||
}
|
||||
|
||||
macro_rules! registration_tests_affine {
|
||||
($($name:ident: $t:ty $(,)?)*) => {
|
||||
$(
|
||||
#[test]
|
||||
fn $name() -> Result<()> {
|
||||
let j = julia_image(0f32, 0f32)?.mapv(|x| x as $t);
|
||||
let shape = j.shape();
|
||||
let origin = [
|
||||
((shape[1] - 1) as f64) / 2f64,
|
||||
((shape[0] - 1) as f64) / 2f64,
|
||||
];
|
||||
let s = AffineTransform::new([1.2, 0., 0., 1., 5., 7.], origin, [shape[0], shape[1]]);
|
||||
let k: Array2<_> = s.transform_image_bspline(j.view())?.into();
|
||||
let t = AffineTransform::register_affine(j.view(), k.view())?.inverse()?;
|
||||
let d = (t.matrix() - s.matrix()).powi(2).sum();
|
||||
assert!(d < 0.025, "d: {}, t: {:?}", d, t.parameters);
|
||||
Ok(())
|
||||
}
|
||||
)*
|
||||
}
|
||||
}
|
||||
|
||||
registration_tests_affine! {
|
||||
registration_tests_affine_u8: u8,
|
||||
registration_tests_affine_i8: i8,
|
||||
registration_tests_affine_u16: u16,
|
||||
registration_tests_affine_i16: i16,
|
||||
registration_tests_affine_u32: u32,
|
||||
registration_tests_affine_i32: i32,
|
||||
registration_tests_affine_u64: u64,
|
||||
registration_tests_affine_i64: i64,
|
||||
registration_tests_affine_f32: f32,
|
||||
registration_tests_affine_f64: f64,
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user