Crate cv_convert

Expand description

Data conversion among computer vision libraries.

Version Selection

This crate supports multiple dependency versions to choose from. The choices of dependency versions are named accordingly as Cargo features. For example, the feature nalgebra_0-30 enables nalgebra 0.30.x. It allows to list crate version selections in Cargo.toml.

[dependencies.cv-convert]
version = 'x.y.z'
features = [
    'image_0-23',
    'opencv_0-62',
    'tch_0-6',
    'nalgebra_0-30',
    'ndarray_0-15',
]

Enable full feature if you wish to enable all crates with up-to-date versions.

Traits

The traits FromCv and IntoCv provide .from_cv() and .into_cv(), and traits TryFromCv and TryIntoCv provide .try_from_cv() and .try_into_cv() methods respectively. Just like std’s From, Into, TryFromCv and TryIntoCv.

use cv_convert::{FromCv, IntoCv, TryFromCv, TryIntoCv};
use nalgebra as na;
use opencv as cv;

// FromCv
let cv_point = cv::core::Point2d::new(1.0, 3.0);
let na_points = na::Point2::<f64>::from_cv(&cv_point);

// IntoCv
let cv_point = cv::core::Point2d::new(1.0, 3.0);
let na_points: na::Point2<f64> = cv_point.into_cv();

// TryFromCv
let na_mat = na::DMatrix::from_vec(2, 3, vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
let cv_mat = cv::core::Mat::try_from_cv(&na_mat).unwrap();

// TryIntoCv
let na_mat = na::DMatrix::from_vec(2, 3, vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
let cv_mat: cv::core::Mat = na_mat.try_into_cv().unwrap();

//!

Supported conversions

The notations are used for simplicity.

S -> T suggests the conversion is defined by non-fallible FromCv.
S ->? T suggests the conversion is defined by fallible TryFromCv.
(&)T means the type can be either owned or borrowed.
&'a S -> &'a T suggests that the target type borrows the source type.

opencv -> opencv

std -> tch

owned/borrowed multi-dimensional array ->? Tensor
- (&)[T; N] ->? Tensor
- (&)[[T; N2]; N1] ->? Tensor
- … and so on up to 6 dimensions

tch -> std

&’a Tensor -> &’a multi-dimensional array
- &’a Tensor -> &’a [T; N]
- &’a Tensor -> &’a [[T; N2]; N1]
- … and so on up to 6 dimensions
(&)Tensor -> owned multi-dimensional array
- (&)Tensor ->? [T; N]
- (&)Tensor ->? [[T; N2]; N1]
- … and so on up to 6 dimensions

opencv -> nalgebra

nalgebra -> opencv

opencv -> tch

(&)Mat ->? Tensor
Mat ->? TensorFromMat

tch -> opencv

(&)Tensor ->? Mat
(&)TensorAsImage ->? Mat

OpenCV

If your system requires opencv/clang-runtime to build, enable the opencv_0-62-clang-runtime feature to solve. Other versions are named accordingly.