# Copyright (c) 2026 Centre National d'Etudes Spatiales (CNES).
#
# This file is part of PANDORA2D
#
# https://github.com/CNES/Pandora2D
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
# limitations under the License.
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# pylint: skip-file
import numpy as np
from numpy.typing import NDArray
from ..interpolation_filter_cpp.interpolation_filter_bind import AbstractFilter
[docs]
def compute_dichotomy_float(
cost_volume: NDArray[np.floating],
disparity_map_col: NDArray[np.floating],
disparity_map_row: NDArray[np.floating],
score_map: NDArray[np.floating],
invalid_map: NDArray[np.floating],
criteria_map: NDArray[np.floating],
subpixel: int,
nb_iterations: int,
filter: AbstractFilter,
method_matching_cost: str,
) -> None:
"""
Dichotomy calculation with float data
:param cost_volume: cost volume data
:param disparity_map_col: column disparity map data
:param disparity_map_row: row disparity map data
:param score_map: score map data
:param invalid_map: invalid map data
:param criteria_map: criteria map data
:param subpixel: sub-sampling of cost_volume
:param nb_iterations: number of iterations of the dichotomy
:param filter: interpolation filter
:param method_matching_cost: max or min
"""
[docs]
def compute_dichotomy_double(
cost_volume: NDArray[np.floating],
disparity_map_col: NDArray[np.floating],
disparity_map_row: NDArray[np.floating],
score_map: NDArray[np.floating],
invalid_map: NDArray[np.floating],
criteria_map: NDArray[np.floating],
subpixel: int,
nb_iterations: int,
filter: AbstractFilter,
method_matching_cost: str,
) -> None:
"""
Dichotomy calculation with double data
:param cost_volume: cost volume data
:param disparity_map_col: column disparity map data
:param disparity_map_row: row disparity map data
:param score_map: score map data
:param invalid_map: invalid map data
:param criteria_map: criteria map data
:param subpixel: sub-sampling of cost_volume
:param nb_iterations: number of iterations of the dichotomy
:param filter: interpolation filter
:param method_matching_cost: max or min
"""