iris.nodes.geometry_estimation package

Submodules

iris.nodes.geometry_estimation.fusion_extrapolation module

class iris.nodes.geometry_estimation.fusion_extrapolation.FusionExtrapolation(circle_extrapolation: ~iris.io.class_configs.Algorithm = <iris.nodes.geometry_estimation.linear_extrapolation.LinearExtrapolation object>, ellipse_fit: ~iris.io.class_configs.Algorithm = <iris.nodes.geometry_estimation.lsq_ellipse_fit_with_refinement.LSQEllipseFitWithRefinement object>, algorithm_switch_std_threshold: float = 3.5, callbacks: ~typing.List[~iris.callbacks.callback_interface.Callback] = [])[source]

Bases: Algorithm

Algorithm implements fusion extrapolation that consist of two concreate extrapolation algoriths.

  1. circle extrapolation algorithm - linear extrapolation algorithm

  2. ellipse extrapolation algorithm - least square ellipse fit with iris polygon refinement.

By default the linear extrapolation algorithm is used but if standard deviation of radiuses is greater than given threshold then least square ellipse fit algorithm is applied because eye is ver likely to be more elliptical then circular.

class Parameters(*, circle_extrapolation: Algorithm, ellipse_fit: Algorithm, algorithm_switch_std_threshold: float)[source]

Bases: Parameters

Parameters of fusion extrapolation algorithm.

algorithm_switch_std_threshold: float
circle_extrapolation: Algorithm
ellipse_fit: Algorithm
run(input_polygons: GeometryPolygons, eye_center: EyeCenters) → GeometryPolygons[source]

Perform extrapolation algorithm.

Parameters:
Returns:

Extrapolated polygons

Return type:

GeometryPolygons

iris.nodes.geometry_estimation.linear_extrapolation module

class iris.nodes.geometry_estimation.linear_extrapolation.LinearExtrapolation(dphi: float = 0.9, callbacks: List[Callback] = [])[source]

Bases: Algorithm

Implementation of geometry estimation algorithm through linear extrapolation in polar space.

Algorithm depends on np.interp therefore it’s better to perform smoothing beforehand.

Algorith steps:

  1. Map iris/pupil polygon vertices to polar space based on estimated iris/pupil centers.

  2. For iris/pupil, perform function interpolation in polar space to estimate missing circle points.

    Note: interpolation in polar space is extrapolation in cartesian space.

  3. Take 2 * np.pi / dphi points from function in polar space.

  4. Map iris/pupil points from polar space back to cartesian space.

class Parameters(*, dphi: ConstrainedFloatValue)[source]

Bases: Parameters

Parameters of linear extrapolation algorithm.

dphi: float
run(input_polygons: GeometryPolygons, eye_center: EyeCenters) → GeometryPolygons[source]

Estimate contours.

Parameters:
Returns:

Extrapolated contours.

Return type:

GeometryPolygons

iris.nodes.geometry_estimation.lsq_ellipse_fit_with_refinement module

class iris.nodes.geometry_estimation.lsq_ellipse_fit_with_refinement.LSQEllipseFitWithRefinement(dphi: float = 1.0, callbacks: List[Callback] = [])[source]

Bases: Algorithm

Algorithm that implements least square ellipse fit with iris polygon refinement by finding points to refine by computing euclidean distance.

Algorithm steps:

  1. Use OpenCV’s fitEllipse method to fit an ellipse to predicted iris and pupil polygons.

  2. Refine predicted pupil polygons points to their original location to prevent location precision loss for those points which were predicted by semseg algorithm.

class Parameters(*, dphi: ConstrainedFloatValue)[source]

Bases: Parameters

Parameters of least square ellipse fit extrapolation algorithm.

dphi: float
static parametric_ellipsis(a: float, b: float, x0: float, y0: float, theta: float, nb_step: int = 100) → ndarray[source]

Given the parameters of a general ellipsis, returns an array of points in this ellipsis.

Parameters:

  • a (float) – Major axis length.

  • b (float) – Minor axis length.

  • x0 (float) – x offset.

  • y0 (float) – y offset.

  • theta (float) – rotation of the ellipsis.

  • nb_step (int) – number of points in the ellipsis.

Returns:

points within the ellipsis.

Return type:

np.ndarray

run(input_polygons: GeometryPolygons) → GeometryPolygons[source]

Estimate extrapolated polygons with OpenCV’s method fitEllipse.

Parameters:

input_polygons (GeometryPolygons) – Smoothed polygons.

Returns:

Extrapolated polygons.

Return type:

GeometryPolygons

Module contents