Merge pull request #17 from rust-cv/maintainance

Maintainance

Author: vadixidav

Cargo.toml

@@ -12,13 +12,16 @@ license = "MIT"
 readme = "README.md"
 
 [features]
-default = ["nalgebra", "nalgebra_std", "ndarray", "image"]
-nalgebra_std = ["nalgebra/std"]
+default = ["alloc", "nalgebra", "ndarray", "image"]
+alloc = ["nalgebra?/alloc"]
+nalgebra = ["dep:nalgebra"]
+ndarray = ["dep:ndarray", "alloc"]
+image = ["dep:image", "alloc"]
 
 [dependencies]
-ndarray = { version = "0.15.4", default-features = false, optional = true }
-nalgebra = { version = "0.30.1", default-features = false, optional = true }
-image = { version = "0.24.0", default-features = false, optional = true }
+ndarray = { version = "0.16", default-features = false, optional = true }
+nalgebra = { version = "0.33", default-features = false, optional = true }
+image = { version = "0.25", default-features = false, optional = true }
 
 [package.metadata.docs.rs]
 all-features = true

README.md

@@ -19,17 +19,16 @@
 
 Provides traits that allow conversion between n-dimensional types in different Rust crates
 
-**NOTE**: By default, this crate includes no conversions. You must choose which crates you want to use using the features:
+**NOTE**: By default, this crate includes conversions for all supported crates. If you want to limit compilation, use `no-default-features = true` enable the corresponding feature for each dependency:
 
-* `ndarray`
 * `nalgebra`
+* `ndarray`
 * `image`
 
-When crates are included, any available conversions between the enabled crates are turned on.
+When two crate features are enabled, any available conversions between the two crates are turned on.
+
+## Limitations
 
 Right now this crate really only provides conversions to owned and borrowed ndarray types. Some limitations exist with `nalgebra`, as it only utilizes positive strides, while `ndarray` supports negative strides as well. The `image` crate has no concept of strides. Due to this, the `ndarray` crate is the most flexible, and is ideal for interoperability between these various crates.
 
-## Supported Crates
-* `image`
-* `ndarray`
-* `nalgebra`
+`nalgebra` currently does not offer a solution to directly pass it an owned vector from `ndarray`, so `into` conversions do perform a copy. It is recommended to create the owned copy in `nalgebra` and then borrow a mutable array view of it using ndarray. You can then populate it accordingly without any copies of the data.

src/toimage.rs

@@ -2,7 +2,7 @@
 ///
 /// This uses an associated type to avoid ambiguity for the compiler.
 /// By calling this, the compiler always knows the returned type.
-pub trait ToImageLuma {
+pub trait IntoImageLuma {
     type Out;
 
     fn into_image_luma(self) -> Self::Out;

src/tonalgebra.rs

@@ -1,11 +1,11 @@
-#[cfg(all(feature = "ndarray", feature = "nalgebra_std"))]
+#[cfg(feature = "ndarray")]
 mod ndarray_impl;
 
 /// Converts a 1 or 2 dimensional type to a nalgebra type.
 ///
 /// This uses an associated type to avoid ambiguity for the compiler.
 /// By calling this, the compiler always knows the returned type.
-pub trait ToNalgebra {
+pub trait IntoNalgebra {
     type Out;
 
     fn into_nalgebra(self) -> Self::Out;

src/tonalgebra/ndarray_impl.rs

@@ -3,189 +3,185 @@
 use super::*;
 
 use core::convert::TryFrom;
-use nalgebra::Dynamic as Dy;
+use nalgebra::Dyn;
 
 /// ```
-/// use nshare::ToNalgebra;
+/// use nshare::IntoNalgebra;
 ///
-/// let arr = ndarray::arr1(&[0.1, 0.2, 0.3, 0.4]);
+/// let arr = ndarray::array![0.1, 0.2, 0.3, 0.4];
 /// let m = arr.view().into_nalgebra();
 /// assert!(m.iter().eq(&[0.1, 0.2, 0.3, 0.4]));
 /// assert_eq!(m.shape(), (4, 1));
 /// ```
-impl<'a, T> ToNalgebra for ndarray::ArrayView1<'a, T>
+impl<'a, T> IntoNalgebra for ndarray::ArrayView1<'a, T>
 where
     T: nalgebra::Scalar,
 {
-    type Out = nalgebra::DVectorSlice<'a, T>;
+    type Out = nalgebra::DVectorView<'a, T>;
     fn into_nalgebra(self) -> Self::Out {
-        let len = Dy::new(self.len());
+        let len = Dyn(self.len());
         let ptr = self.as_ptr();
         let stride: usize = TryFrom::try_from(self.strides()[0]).expect("Negative stride");
         let storage = unsafe {
-            nalgebra::SliceStorage::from_raw_parts(
+            nalgebra::ViewStorage::from_raw_parts(
                 ptr,
                 (len, nalgebra::Const::<1>),
-                (nalgebra::Const::<1>, Dy::new(stride)),
+                (nalgebra::Const::<1>, Dyn(stride)),
             )
         };
         nalgebra::Matrix::from_data(storage)
     }
 }
 /// ```
-/// use nshare::ToNalgebra;
+/// use nshare::IntoNalgebra;
 ///
-/// let mut arr = ndarray::arr1(&[0.1, 0.2, 0.3, 0.4]);
+/// let mut arr = ndarray::array![0.1, 0.2, 0.3, 0.4];
 /// let m = arr.view_mut().into_nalgebra();
 /// assert!(m.iter().eq(&[0.1, 0.2, 0.3, 0.4]));
 /// assert_eq!(m.shape(), (4, 1));
 /// ```
-impl<'a, T> ToNalgebra for ndarray::ArrayViewMut1<'a, T>
+impl<'a, T> IntoNalgebra for ndarray::ArrayViewMut1<'a, T>
 where
     T: nalgebra::Scalar,
 {
-    type Out = nalgebra::DVectorSliceMut<'a, T>;
+    type Out = nalgebra::DVectorViewMut<'a, T>;
     fn into_nalgebra(mut self) -> Self::Out {
-        let len = Dy::new(self.len());
+        let len = Dyn(self.len());
         let stride: usize = TryFrom::try_from(self.strides()[0]).expect("Negative stride");
         let ptr = self.as_mut_ptr();
         let storage = unsafe {
-            // Drop to not have simultaneously the ndarray and nalgebra valid.
-            drop(self);
-            nalgebra::SliceStorageMut::from_raw_parts(
+            nalgebra::ViewStorageMut::from_raw_parts(
                 ptr,
                 (len, nalgebra::Const::<1>),
-                (nalgebra::Const::<1>, Dy::new(stride)),
+                (nalgebra::Const::<1>, Dyn(stride)),
             )
         };
         nalgebra::Matrix::from_data(storage)
     }
 }
 
 /// ```
-/// use nshare::ToNalgebra;
+/// use nshare::IntoNalgebra;
 ///
-/// let arr = ndarray::arr1(&[0.1, 0.2, 0.3, 0.4]);
+/// let arr = ndarray::array![0.1, 0.2, 0.3, 0.4];
 /// let m = arr.into_nalgebra();
 /// assert!(m.iter().eq(&[0.1, 0.2, 0.3, 0.4]));
 /// assert_eq!(m.shape(), (4, 1));
 /// ```
-impl<T> ToNalgebra for ndarray::Array1<T>
+impl<T> IntoNalgebra for ndarray::Array1<T>
 where
     T: nalgebra::Scalar,
 {
     type Out = nalgebra::DVector<T>;
     fn into_nalgebra(self) -> Self::Out {
-        let len = Dy::new(self.len());
-        Self::Out::from_vec_generic(len, nalgebra::Const::<1>, self.into_raw_vec())
+        let len = Dyn(self.len());
+        // There is no method to give nalgebra the vector directly where it isn't allocated. If you call
+        // from_vec_generic, it simply calls from_iterator_generic which uses Iterator::collect(). Due to this,
+        // the simplest solution is to just pass an iterator over the values. If you come across this because you
+        // have a performance issue, I would recommend creating the owned data using naglebra and borrowing it with
+        // ndarray to perform operations on it instead of the other way around.
+        Self::Out::from_iterator_generic(len, nalgebra::Const::<1>, self.iter().cloned())
     }
 }
 
 /// ```
-/// use nshare::ToNalgebra;
+/// use nshare::IntoNalgebra;
 ///
-/// let arr = ndarray::arr2(&[
+/// let arr = ndarray::array![
 ///     [0.1, 0.2, 0.3, 0.4],
 ///     [0.5, 0.6, 0.7, 0.8],
 ///     [1.1, 1.2, 1.3, 1.4],
 ///     [1.5, 1.6, 1.7, 1.8],
-/// ]);
+/// ];
 /// let m = arr.view().into_nalgebra();
 /// assert!(m.row(1).iter().eq(&[0.5, 0.6, 0.7, 0.8]));
 /// assert_eq!(m.shape(), (4, 4));
 /// assert!(arr.t().into_nalgebra().column(1).iter().eq(&[0.5, 0.6, 0.7, 0.8]));
 /// ```
-impl<'a, T> ToNalgebra for ndarray::ArrayView2<'a, T>
+impl<'a, T> IntoNalgebra for ndarray::ArrayView2<'a, T>
 where
     T: nalgebra::Scalar,
 {
-    type Out = nalgebra::DMatrixSlice<'a, T, Dy, Dy>;
+    type Out = nalgebra::DMatrixView<'a, T, Dyn, Dyn>;
     fn into_nalgebra(self) -> Self::Out {
-        let nrows = Dy::new(self.nrows());
-        let ncols = Dy::new(self.ncols());
+        let nrows = Dyn(self.nrows());
+        let ncols = Dyn(self.ncols());
         let ptr = self.as_ptr();
-        let stride_row: usize = TryFrom::try_from(self.strides()[0]).expect("Negative row stride");
-        let stride_col: usize =
-            TryFrom::try_from(self.strides()[1]).expect("Negative column stride");
+        let stride_row: usize = TryFrom::try_from(self.strides()[0])
+            .expect("can only convert positive row stride to nalgebra");
+        let stride_col: usize = TryFrom::try_from(self.strides()[1])
+            .expect("can only convert positive col stride to nalgebra");
         let storage = unsafe {
-            nalgebra::SliceStorage::from_raw_parts(
+            nalgebra::ViewStorage::from_raw_parts(
                 ptr,
                 (nrows, ncols),
-                (Dy::new(stride_row), Dy::new(stride_col)),
+                (Dyn(stride_row), Dyn(stride_col)),
             )
         };
         nalgebra::Matrix::from_data(storage)
     }
 }
 
 /// ```
-/// use nshare::ToNalgebra;
+/// use nshare::IntoNalgebra;
 ///
-/// let mut arr = ndarray::arr2(&[
+/// let mut arr = ndarray::array![
 ///     [0.1, 0.2, 0.3, 0.4],
 ///     [0.5, 0.6, 0.7, 0.8],
 ///     [1.1, 1.2, 1.3, 1.4],
 ///     [1.5, 1.6, 1.7, 1.8],
-/// ]);
+/// ];
 /// let m = arr.view_mut().into_nalgebra();
 /// assert!(m.row(1).iter().eq(&[0.5, 0.6, 0.7, 0.8]));
 /// assert_eq!(m.shape(), (4, 4));
 /// assert!(arr.view_mut().reversed_axes().into_nalgebra().column(1).iter().eq(&[0.5, 0.6, 0.7, 0.8]));
 /// ```
-impl<'a, T> ToNalgebra for ndarray::ArrayViewMut2<'a, T>
+impl<'a, T> IntoNalgebra for ndarray::ArrayViewMut2<'a, T>
 where
     T: nalgebra::Scalar,
 {
-    type Out = nalgebra::DMatrixSliceMut<'a, T, Dy, Dy>;
+    type Out = nalgebra::DMatrixViewMut<'a, T, Dyn, Dyn>;
     fn into_nalgebra(mut self) -> Self::Out {
-        let nrows = Dy::new(self.nrows());
-        let ncols = Dy::new(self.ncols());
-        let stride_row: usize = TryFrom::try_from(self.strides()[0]).expect("Negative row stride");
-        let stride_col: usize =
-            TryFrom::try_from(self.strides()[1]).expect("Negative column stride");
+        let nrows = Dyn(self.nrows());
+        let ncols = Dyn(self.ncols());
+        let stride_row: usize = TryFrom::try_from(self.strides()[0])
+            .expect("can only convert positive row stride to nalgebra");
+        let stride_col: usize = TryFrom::try_from(self.strides()[1])
+            .expect("can only convert positive col stride to nalgebra");
         let ptr = self.as_mut_ptr();
         let storage = unsafe {
-            // Drop to not have simultaneously the ndarray and nalgebra valid.
-            drop(self);
-            nalgebra::SliceStorageMut::from_raw_parts(
+            nalgebra::ViewStorageMut::from_raw_parts(
                 ptr,
                 (nrows, ncols),
-                (Dy::new(stride_row), Dy::new(stride_col)),
+                (Dyn(stride_row), Dyn(stride_col)),
             )
         };
         nalgebra::Matrix::from_data(storage)
     }
 }
 
 /// ```
-/// use nshare::ToNalgebra;
+/// use nshare::IntoNalgebra;
 ///
-/// let mut arr = ndarray::arr2(&[
+/// let mut arr = ndarray::array![
 ///     [0.1, 0.2, 0.3, 0.4],
 ///     [0.5, 0.6, 0.7, 0.8],
 ///     [1.1, 1.2, 1.3, 1.4],
 ///     [1.5, 1.6, 1.7, 1.8],
-/// ]);
+/// ];
 /// let m = arr.clone().into_nalgebra();
 /// assert!(m.row(1).iter().eq(&[0.5, 0.6, 0.7, 0.8]));
 /// assert_eq!(m.shape(), (4, 4));
 /// assert!(arr.reversed_axes().into_nalgebra().column(1).iter().eq(&[0.5, 0.6, 0.7, 0.8]));
 /// ```
-impl<T> ToNalgebra for ndarray::Array2<T>
+impl<T> IntoNalgebra for ndarray::Array2<T>
 where
     T: nalgebra::Scalar,
 {
     type Out = nalgebra::DMatrix<T>;
     fn into_nalgebra(self) -> Self::Out {
-        let std_layout = self.is_standard_layout();
-        let nrows = Dy::new(self.nrows());
-        let ncols = Dy::new(self.ncols());
-        let mut res = Self::Out::from_vec_generic(nrows, ncols, self.into_raw_vec());
-        if std_layout {
-            // This can be expensive, but we have no choice since nalgebra VecStorage is always
-            // column-based.
-            res.transpose_mut();
-        }
-        res
+        let nrows = Dyn(self.nrows());
+        let ncols = Dyn(self.ncols());
+        Self::Out::from_iterator_generic(nrows, ncols, self.t().iter().cloned())
     }
 }