Vanishing point estimation

Instantiate a vanishing point detector

limap.image.groups.vplib.get_vp_detector(method: str, vpoptions: DetectorOptions): Get a vanishing point detector

class limap.image.groups.vplib.DetectorOptions(base_options: limap._limap._image._groups._vplib.BaseVPDetectorOptions = <factory>, jlinkage_options: limap.image.groups.vplib.register_vp_detector.JLinkageOptions = <factory>)

__init__(base_options: BaseVPDetectorOptions = <factory>, jlinkage_options: JLinkageOptions = <factory>) → None

base_options: BaseVPDetectorOptions

jlinkage_options: JLinkageOptions

class limap.image.groups.vplib.register_vp_detector.JLinkageOptions(th_perp_supports: float = 3.0)

__init__(th_perp_supports: float = 3.0) → None

th_perp_supports: float = 3.0

Base interface

class limap.image.groups.vplib.base_vp_detector.BaseVPDetector(options=BaseVPDetectorOptions(min_length=40.0, inlier_threshold=1.0, min_num_supports=5, n_jobs=-1))

__init__(options=BaseVPDetectorOptions(min_length=40.0, inlier_threshold=1.0, min_num_supports=5, n_jobs=-1))

detect_vp(lines: list[Line2d], image_path: Path | None = None) → VPResult: Virtual method (need to be implemented) - detect vanishing points

detect_vp_all_images(all_lines: dict[int, list[Line2d]], image_paths: dict[int, Path] | None = None) → dict[int, VPResult]: Detect vanishing points on multiple images with multiple processes

get_module_name() → str: Virtual method (need to be implemented) - return the name of the module

visualize(fname, img, lines, vpresult, show_original=False, endpoints=False)

class limap.image.groups.vplib.BaseVPDetectorOptions

__init__(*args, **kwargs)

Overloaded function.

__init__(self: limap._limap._image._groups._vplib.BaseVPDetectorOptions) -> None
__init__(self: limap._limap._image._groups._vplib.BaseVPDetectorOptions, kwargs: dict) -> None
__init__(self: limap._limap._image._groups._vplib.BaseVPDetectorOptions, **kwargs) -> None

mergedict(self: object, kwargs: dict) → None

summary(self: limap._limap._image._groups._vplib.BaseVPDetectorOptions, write_type: bool = False) → str

todict(self: limap._limap._image._groups._vplib.BaseVPDetectorOptions, recursive: bool = True) → dict

property inlier_threshold

1.0)

Type:: (float, default

property min_length

40.0)

Type:: (float, default

property min_num_supports

Type:: (int, default

property n_jobs

-1)

Type:: (int, default

Results

A detector returns a VPResult per image, which the frontend converts into the 2D groups stored in the structure database.

class limap.image.groups.vplib.VPResult

__init__(*args, **kwargs)

Overloaded function.

__init__(self: limap._limap._image._groups._vplib.VPResult) -> None
__init__(self: limap._limap._image._groups._vplib.VPResult, arg0: collections.abc.Sequence[typing.SupportsInt | typing.SupportsIndex], arg1: collections.abc.Sequence[typing.Annotated[numpy.typing.ArrayLike, numpy.float64, “[3, 1]”]]) -> None
__init__(self: limap._limap._image._groups._vplib.VPResult, arg0: limap._limap._image._groups._vplib.VPResult) -> None
__init__(self: limap._limap._image._groups._vplib.VPResult, kwargs: dict) -> None
__init__(self: limap._limap._image._groups._vplib.VPResult, **kwargs) -> None

count_lines(self: limap._limap._image._groups._vplib.VPResult) → int

count_vps(self: limap._limap._image._groups._vplib.VPResult) → int

get_vp(self: limap._limap._image._groups._vplib.VPResult, line_id: SupportsInt | SupportsIndex) → Annotated[numpy.typing.NDArray[numpy.float64], '[3, 1]']

get_vp_label(self: limap._limap._image._groups._vplib.VPResult, line_id: SupportsInt | SupportsIndex) → int

get_vp_params(self: limap._limap._image._groups._vplib.VPResult, vp_id: SupportsInt | SupportsIndex) → Annotated[numpy.typing.NDArray[numpy.float64], '[3, 1]']

has_vp(self: limap._limap._image._groups._vplib.VPResult, line_id: SupportsInt | SupportsIndex) → bool

mergedict(self: object, kwargs: dict) → None

summary(self: limap._limap._image._groups._vplib.VPResult, write_type: bool = False) → str

todict(self: limap._limap._image._groups._vplib.VPResult, recursive: bool = True) → dict

property labels

[])

Type:: (list, default

property vps

[])

Type:: (list, default

limap.image.groups.vplib.convert_vpresult_to_groups2d(vpresult: limap._limap._image._groups._vplib.VPResult) → list[limap._limap._scene.Group2d]

limap.image.groups.vplib.convert_vpresults_to_groups2d(vpresults: boost::unordered::unordered_flat_map<unsigned int, limap: :image::groups::vplib::VPResult, std: :hash<unsigned int>, std: :equal_to<unsigned int>, std: :allocator<std::pair<unsigned int const, limap: :image::groups::vplib::VPResult> > >) → boost::unordered::unordered_flat_map<unsigned int, std::vector<limap::Group2d, std::allocator<limap::Group2d> >, std::hash<unsigned int>, std::equal_to<unsigned int>, std::allocator<std::pair<unsigned int const, std::vector<limap::Group2d, std::allocator<limap::Group2d> > > > >

JLinkage

The bundled J-Linkage implementation. Its parameters (JLinkageOptions here) are distinct from the registry-level JLinkageOptions above.

class limap.image.groups.vplib.JLinkageOptions

__init__(*args, **kwargs)

Overloaded function.

__init__(self: limap._limap._image._groups._vplib.JLinkageOptions) -> None
__init__(self: limap._limap._image._groups._vplib.JLinkageOptions, kwargs: dict) -> None
__init__(self: limap._limap._image._groups._vplib.JLinkageOptions, **kwargs) -> None

mergedict(self: object, kwargs: dict) → None

summary(self: limap._limap._image._groups._vplib.JLinkageOptions, write_type: bool = False) → str

todict(self: limap._limap._image._groups._vplib.JLinkageOptions, recursive: bool = True) → dict

property base_options

BaseVPDetectorOptions(min_length=40.0, inlier_threshold=1.0, min_num_supports=5, n_jobs=-1))

Type:: (BaseVPDetectorOptions, default

property th_perp_supports

3.0)

Type:: (float, default

class limap.image.groups.vplib.JLinkage

__init__(*args, **kwargs)

Overloaded function.

__init__(self: limap._limap._image._groups._vplib.JLinkage) -> None
__init__(self: limap._limap._image._groups._vplib.JLinkage, arg0: limap._limap._image._groups._vplib.JLinkageOptions) -> None
__init__(self: limap._limap._image._groups._vplib.JLinkage, kwargs: dict) -> None
__init__(self: limap._limap._image._groups._vplib.JLinkage, **kwargs) -> None

associate_vps(self: limap._limap._image._groups._vplib.JLinkage, arg0: collections.abc.Sequence[limap._limap._geometry.Line2d]) → limap._limap._image._groups._vplib.VPResult

associate_vps_parallel(self: limap._limap._image._groups._vplib.JLinkage, arg0: collections.abc.Mapping[SupportsInt | SupportsIndex, collections.abc.Sequence[limap._limap._geometry.Line2d]]) → dict[int, limap._limap._image._groups._vplib.VPResult]

compute_vp_labels(self: limap._limap._image._groups._vplib.JLinkage, arg0: collections.abc.Sequence[limap._limap._geometry.Line2d]) → list[int]

mergedict(self: object, kwargs: dict) → None

summary(self: limap._limap._image._groups._vplib.JLinkage, write_type: bool = False) → str

todict(self: limap._limap._image._groups._vplib.JLinkage, recursive: bool = True) → dict