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ShevchenkoRostyslavycbiometricswiktorlazarskiycbiometrics
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Release 1.9.2 (#147)
* tets workflow * upd * add prod workflow * correct the repo name * upd name * add a workflow to create docs * address PR comments * debugging * add requirements * upd deploy docs * docs branch * fix bug * cleaning * new way * fake upd of docds * upd * upd * rollback * upd * upd * revert * worktree * another approach * upd * keep some files * final version * swap the order * upd * upd * upd * remove .buildinfo * remove .buildinfo * cleaning * Iris multiview/aic 5901: e2e Multiframe Iris Pipeline (#123) * refactor multiframe iris pipeline * refactor and fix tests * fix test leakage from patches * upd e2e tests * fix broken tests * rename MultiframeAggregationPipeline -> TemplatesAggregation * Add user warning * bump the iris version * 1.8.3 -> 1.9.0 (#137) Co-authored-by: ycbiometrics <107422087+ycbiometrics@users.noreply.github.com> * Make sure that NodeWritterResult callback is always first in callbacks list of every node. * Hotfix/output builders type (#139) * 1.8.3 -> 1.9.0 * bugfix serialization data type * add speciofic e2e test for multiframe * revert the doc file from 1.9.0 releqase * Hotfix/output builders type (#141) * 1.8.3 -> 1.9.0 * release v1.9.0 (#136) * tets workflow * upd * add prod workflow * correct the repo name * upd name * add a workflow to create docs * address PR comments * debugging * add requirements * upd deploy docs * docs branch * fix bug * cleaning * new way * fake upd of docds * upd * upd * rollback * upd * upd * revert * worktree * another approach * upd * keep some files * final version * swap the order * upd * upd * upd * remove .buildinfo * remove .buildinfo * cleaning * Iris multiview/aic 5901: e2e Multiframe Iris Pipeline (#123) * refactor multiframe iris pipeline * refactor and fix tests * fix test leakage from patches * upd e2e tests * fix broken tests * rename MultiframeAggregationPipeline -> TemplatesAggregation * Add user warning * bump the iris version * 1.8.3 -> 1.9.0 (#137) Co-authored-by: ycbiometrics <107422087+ycbiometrics@users.noreply.github.com> --------- Co-authored-by: ycbiometrics <107422087+ycbiometrics@users.noreply.github.com> * bugfix serialization data type * add speciofic e2e test for multiframe * revert the doc file from 1.9.0 releqase * update iris version * upd --------- Co-authored-by: ycbiometrics <107422087+ycbiometrics@users.noreply.github.com> * Preserve ndarray in safe serialization; bump IRIS to 1.9.2 (#145) * fix: not to cast ndarray to list * fix tests * bump the version * rollback docs * Yichen1 aic 6094 pr for new interpolation fusion method (#144) * improve the fusion methold on boundary extrapolation * update more config files * update unit tests --------- Co-authored-by: ycbiometrics <yi.chen@ip-10-54-144-12.eu-central-1.compute.internal> --------- Co-authored-by: ycbiometrics <107422087+ycbiometrics@users.noreply.github.com> Co-authored-by: Wiktor Łazarski <wjlazarski@gmail.com> Co-authored-by: ycbiometrics <yi.chen@ip-10-54-144-12.eu-central-1.compute.internal>
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‎docs/source/_code_subpages/iris.pipelines.rst‎

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@@ -23,7 +23,7 @@ iris.pipelines.iris\_pipeline module
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iris.pipelines.multiframe\_aggregation\_pipeline module
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-------------------------------------------------------
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.. automodule:: iris.pipelines.templates_aggregation_pipeline
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.. automodule:: iris.pipelines.multiframe_aggregation_pipeline
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:members:
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:undoc-members:
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:show-inheritance:

‎src/iris/_version.py‎

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__version__ = "1.9.1"
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__version__ = "1.9.2"

‎src/iris/nodes/geometry_estimation/fusion_extrapolation.py‎

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@@ -1,5 +1,7 @@
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from typing import List
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import numpy as np
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from iris.callbacks.callback_interface import Callback
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from iris.io.class_configs import Algorithm
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from iris.io.dataclasses import EyeCenters, GeometryPolygons
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class FusionExtrapolation(Algorithm):
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"""Algorithm implements fusion extrapolation that consist of two concreate extrapolation algoriths.
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1) circle extrapolation algorithm - linear extrapolation algorithm
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2) ellipse extrapolation algorithm - least square ellipse fit with iris polygon refinement.
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"""Fuse two extrapolation strategies and pick the result based on shape statistics.
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1) Circle-like extrapolation: LinearExtrapolation (linear extrapolation algorithm)
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2) Ellipse-like extrapolation: LSQEllipseFitWithRefinement (least square ellipse fit with iris polygon refinement)
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By default, the linear extrapolation is used. If the (relative) spread of radii
20+
exceeds a threshold and the ellipse-based result looks sufficiently "regular"
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(based on squared radii ratios), we prefer the ellipse fit.
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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.
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Notes:
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- "Relative std" means std/mean with an epsilon guard.
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- We compare regularity via the relative std of iris/pupil squared-radius ratios under three centering choices (circle/circle, ellipse/ellipse, ellipse/circle).
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"""
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class Parameters(Algorithm.Parameters):
@@ -22,33 +31,73 @@ class Parameters(Algorithm.Parameters):
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circle_extrapolation: Algorithm
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ellipse_fit: Algorithm
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algorithm_switch_std_threshold: float
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algorithm_switch_std_conditioned_multiplier: float
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__parameters_type__ = Parameters
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def __init__(
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self,
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circle_extrapolation: Algorithm = LinearExtrapolation(dphi=360 / 512),
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ellipse_fit: Algorithm = LSQEllipseFitWithRefinement(dphi=360 / 512),
32-
algorithm_switch_std_threshold: float = 3.5,
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algorithm_switch_std_threshold: float = 0.014,
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algorithm_switch_std_conditioned_multiplier: float = 2.0,
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callbacks: List[Callback] = [],
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) -> None:
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"""Assign parameters.
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Args:
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circle_extrapolation (Algorithm, optional): More circular shape estimation algorithm. Defaults to LinearExtrapolation(dphi=360 / 512, degrees / width of normalized image).
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ellipse_fit (Algorithm, optional): More elliptical shape estimation algorithm. Defaults to LSQEllipseFitWithRefinement(dphi=360 / 512, degrees / width of normalized image).
40-
algorithm_switch_std_threshold (float, optional): Algorithm switch threshold. Defaults to 3.5.
51+
algorithm_switch_std_threshold (float, optional): Threshold on (rel. std of iris + rel. std of pupil) beyond which we consider switching to ellipse.. Defaults to 0.014.
52+
algorithm_switch_std_conditioned_multiplier (float, optional): How strongly iris spread penalizes circle regularity in the switch condition. Default: 2.0.
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callbacks (List[Callback], optional): _description_. Defaults to [].
4254
"""
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super().__init__(
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ellipse_fit=ellipse_fit,
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circle_extrapolation=circle_extrapolation,
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algorithm_switch_std_threshold=algorithm_switch_std_threshold,
59+
algorithm_switch_std_conditioned_multiplier=algorithm_switch_std_conditioned_multiplier,
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callbacks=callbacks,
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)
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63+
@staticmethod
64+
def _relative_std(x: np.ndarray, eps: float = 1e-12) -> float:
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"""
66+
Relative std = std / max(mean, eps).
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Args:
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x: 1D array-like.
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eps: Small guard to avoid division by zero.
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72+
Returns:
73+
float: relative std.
74+
"""
75+
x = np.asarray(x)
76+
m = float(np.mean(x))
77+
s = float(np.std(x))
78+
return s / max(abs(m), eps)
79+
80+
@staticmethod
81+
def _squared_relative_radii(
82+
iris_centered: np.ndarray, pupil_centered: np.ndarray, eps: float = 1e-12
83+
) -> np.ndarray:
84+
"""
85+
Ratio of iris over pupil squared radii, element wise.
86+
87+
Args:
88+
iris_centered: (N, 2) centered iris points.
89+
pupil_centered: (N, 2) centered pupil points.
90+
eps: Small guard to avoid division by zero.
91+
92+
Returns:
93+
(N,) array: iris_sq / pupil_sq
94+
"""
95+
iris_sq = np.sum(iris_centered**2, axis=1)
96+
pupil_sq = np.sum(pupil_centered**2, axis=1)
97+
return np.divide(iris_sq, np.maximum(pupil_sq, eps))
98+
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def run(self, input_polygons: GeometryPolygons, eye_center: EyeCenters) -> GeometryPolygons:
51-
"""Perform extrapolation algorithm.
100+
"""Perform extrapolation algorithm and select the most plausible result.
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Args:
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input_polygons (GeometryPolygons): Smoothed polygons.
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Returns:
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GeometryPolygons: Extrapolated polygons
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"""
60-
xs, ys = input_polygons.iris_array[:, 0], input_polygons.iris_array[:, 1]
61-
rhos, _ = cartesian2polar(xs, ys, eye_center.iris_x, eye_center.iris_y)
109+
xs_iris, ys_iris = input_polygons.iris_array[:, 0], input_polygons.iris_array[:, 1]
110+
rhos_iris, _ = cartesian2polar(xs_iris, ys_iris, eye_center.iris_x, eye_center.iris_y)
111+
xs_pupil, ys_pupil = input_polygons.pupil_array[:, 0], input_polygons.pupil_array[:, 1]
112+
rhos_pupil, _ = cartesian2polar(xs_pupil, ys_pupil, eye_center.pupil_x, eye_center.pupil_y)
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circle_poly = self.params.circle_extrapolation(input_polygons, eye_center)
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ellipse_poly = self.params.ellipse_fit(input_polygons)
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116+
circle_iris = circle_poly.iris_array
117+
circle_pupil = circle_poly.pupil_array
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ellipse_iris = ellipse_poly.iris_array
119+
ellipse_pupil = ellipse_poly.pupil_array
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circle_center = np.mean(circle_pupil, axis=0)
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ellipse_center = np.mean(ellipse_pupil, axis=0)
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circle_iris_centered = circle_iris - circle_center
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circle_pupil_centered = circle_pupil - circle_center
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ellipse_iris_centered = ellipse_iris - ellipse_center
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ellipse_pupil_centered = ellipse_pupil - ellipse_center
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ec_iris_centered = ellipse_iris - circle_center
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130+
cc = self._squared_relative_radii(circle_iris_centered, circle_pupil_centered)
131+
ee = self._squared_relative_radii(ellipse_iris_centered, ellipse_pupil_centered)
132+
ec = self._squared_relative_radii(ec_iris_centered, circle_pupil_centered)
133+
cc_reg = self._relative_std(cc)
134+
ee_reg = self._relative_std(ee)
135+
ec_reg = self._relative_std(ec)
136+
137+
radius_std_iris = self._relative_std(rhos_iris)
138+
radius_std_pupil = self._relative_std(rhos_pupil)
139+
min_reg = min(ee_reg, ec_reg)
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63-
new_poly = self.params.circle_extrapolation(input_polygons, eye_center)
141+
spread_ok = (radius_std_iris + radius_std_pupil) >= self.params.algorithm_switch_std_threshold
142+
reg_ok = min_reg <= (cc_reg + radius_std_iris * self.params.algorithm_switch_std_conditioned_multiplier)
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radius_std = rhos.std()
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if radius_std > self.params.algorithm_switch_std_threshold:
67-
ellipse_poly = self.params.ellipse_fit(input_polygons)
68-
new_poly = GeometryPolygons(
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pupil_array=new_poly.pupil_array,
70-
iris_array=ellipse_poly.iris_array,
71-
eyeball_array=input_polygons.eyeball_array,
72-
)
144+
if spread_ok and reg_ok:
145+
if ee_reg <= ec_reg:
146+
return GeometryPolygons(
147+
pupil_array=ellipse_poly.pupil_array,
148+
iris_array=ellipse_poly.iris_array,
149+
eyeball_array=input_polygons.eyeball_array,
150+
)
151+
else:
152+
return GeometryPolygons(
153+
pupil_array=circle_poly.pupil_array,
154+
iris_array=ellipse_poly.iris_array,
155+
eyeball_array=input_polygons.eyeball_array,
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)
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74-
return new_poly
158+
return circle_poly

‎src/iris/orchestration/output_builders.py‎

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@@ -69,7 +69,7 @@ def __safe_serialize(object: Optional[Any]) -> Optional[Any]:
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elif isinstance(object, (list, tuple)):
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return type(object)(__safe_serialize(sub_object) for sub_object in object)
7171
elif isinstance(object, np.ndarray):
72-
return object.tolist()
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return object
7373
elif isinstance(object, (str, int, float, bool)):
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return object
7575
else:

‎src/iris/pipelines/confs/multiframe_iris_pipeline.yaml‎

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metadata:
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pipeline_name: multiframe_iris_pipeline
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iris_version: 1.9.1
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iris_version: 1.9.2
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# Configuration for individual image processing (IRISPipeline)
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iris_pipeline:
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metadata:
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pipeline_name: iris_pipeline
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iris_version: 1.9.1
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iris_version: 1.9.2
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pipeline:
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- name: segmentation
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class_name: iris.LSQEllipseFitWithRefinement
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params:
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dphi: 0.703125
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algorithm_switch_std_threshold: 3.5
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algorithm_switch_std_threshold: 0.014
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algorithm_switch_std_conditioned_multiplier: 2.0
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inputs:
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- name: input_polygons
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source_node: smoothing
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# Configuration for template aggregation (TemplatesAggregationPipeline)
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templates_aggregation_pipeline:
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metadata:
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iris_version: 1.9.1
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iris_version: 1.9.2
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pipeline_name: templates_aggregation
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pipeline:

‎src/iris/pipelines/confs/pipeline.yaml‎

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metadata:
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pipeline_name: iris_pipeline
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iris_version: 1.9.1
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iris_version: 1.9.2
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pipeline:
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- name: segmentation
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class_name: iris.LSQEllipseFitWithRefinement
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params:
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dphi: 0.703125
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algorithm_switch_std_threshold: 3.5
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algorithm_switch_std_threshold: 0.014
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algorithm_switch_std_conditioned_multiplier: 2.0
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inputs:
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- name: input_polygons
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source_node: smoothing

‎src/iris/pipelines/confs/templates_aggregation_pipeline.yaml‎

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metadata:
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pipeline_name: iris_pipeline
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iris_version: 1.9.1
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iris_version: 1.9.2
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pipeline:
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- name: segmentation
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class_name: iris.LSQEllipseFitWithRefinement
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params:
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dphi: 0.703125
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algorithm_switch_std_threshold: 3.5
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algorithm_switch_std_threshold: 0.014
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algorithm_switch_std_conditioned_multiplier: 2.0
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inputs:
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- name: input_polygons
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source_node: smoothing
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306307
templates_aggregation:
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metadata:
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iris_version: 1.9.1
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iris_version: 1.9.2
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pipeline_name: templates_aggregation
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pipeline:

‎tests/e2e_tests/nodes/geometry_estimation/mocks/e2e_expected_result_fusion_extrapolation.pickle‎

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‎tests/e2e_tests/pipelines/mocks/outputs/expected_iris_debug_pipeline_output.pickle‎

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‎tests/e2e_tests/pipelines/mocks/outputs/expected_iris_orb_pipeline_output.pickle‎

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