Files
nixpkgs/pkgs/by-name/op/opengv/package.nix
Dark Steveneq 646b892680
Some checks failed
Periodic Merges (6h) / master → staging-nixos (push) Failing after 12m50s
Periodic Merges (6h) / master → staging-next (push) Failing after 12m54s
Periodic Merges (24h) / merge-base(master,staging) → haskell-updates (push) Failing after 11m54s
Periodic Merges (6h) / staging-next → staging (push) Failing after 12m13s
Periodic Merges (24h) / staging-next-25.05 → staging-25.05 (push) Failing after 13m24s
Periodic Merges (24h) / release-25.05 → staging-next-25.05 (push) Failing after 14m28s
push sheeet
2025-10-09 14:15:47 +02:00

45 lines
1.3 KiB
Nix

{
lib,
stdenv,
eigen,
fetchFromGitHub,
cmake,
}:
stdenv.mkDerivation (finalAttrs: {
pname = "opengv";
version = "0-unstable-2020-08-06";
src = fetchFromGitHub {
owner = "laurentkneip";
repo = "opengv";
rev = "91f4b19c73450833a40e463ad3648aae80b3a7f3";
hash = "sha256-LfnylJ9NCHlqjT76Tgku4NwxULJ+WDAcJQ2lDKGWSI4=";
};
nativeBuildInputs = [
cmake
];
buildInputs = [
eigen
];
meta = {
description = "Collection of computer vision methods for solving geometric vision problems";
homepage = "https://github.com/laurentkneip/opengv";
license = lib.licenses.bsd2;
longDescription = ''
OpenGV is a collection of computer vision methods for solving
geometric vision problems. It contains absolute-pose, relative-pose,
triangulation, and point-cloud alignment methods for the calibrated
case. All problems can be solved with central or non-central cameras,
and embedded into a random sample consensus or nonlinear optimization
context. Matlab and Python interfaces are implemented as well. The link
to the above pages also shows links to precompiled Matlab mex-libraries.
Please consult the documentation for more information.
'';
maintainers = [ lib.maintainers.locochoco ];
platforms = lib.platforms.all;
};
})