#!/usr/bin/env python
#
# Copyright (c) 2026 CS GROUP France
#
# 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""
This module contains functions associated to the estimation computation step.
"""
from __future__ import annotations
import logging
import sys
from abc import ABCMeta, abstractmethod
from collections.abc import Callable
import numpy as np
import xarray as xr
if sys.version_info >= (3, 11):
from typing import Self
else:
from typing_extensions import Self
[docs]
class AbstractEstimation:
"""
Abstract Estimation class
"""
[docs]
estimation_methods_avail: dict = {}
[docs]
_estimation_method = None
[docs]
cfg: dict | None = None
def __new__(cls, cfg: dict | None = None):
"""
Return the plugin associated with the estimation_method given in the configuration
:param cfg: configuration {'estimation_method': value}
"""
if cls is AbstractEstimation:
if isinstance(cfg["estimation_method"], str):
try:
return super().__new__(cls.estimation_methods_avail[cfg["estimation_method"]])
except KeyError:
logging.error("No estimation method named %s supported", cfg["estimation_method"])
raise KeyError
else:
if isinstance(cfg["estimation_method"], unicode): # type: ignore # pylint: disable=undefined-variable
# creating a plugin from registered short name given as unicode (py2 & 3 compatibility)
try:
return super().__new__(cls.estimation_methods_avail[cfg["estimation_method"].encode("utf-8")])
except KeyError:
logging.error(
"No estimation method named %s supported",
cfg["estimation_method"],
)
raise KeyError
else:
return super().__new__(cls)
return None
@classmethod
[docs]
def register_subclass(cls, short_name: str) -> Callable[[type[Self]], type[Self]]:
"""
Allows to register the subclass with its short name
:param short_name: the subclass to be registered
"""
def decorator(subclass: type[Self]) -> type[Self]:
"""
Registers the subclass in the available methods
:param subclass: the subclass to be registered
"""
cls.estimation_methods_avail[short_name] = subclass
return subclass
return decorator
[docs]
def desc(self) -> None:
"""
Describes the estimation method
:return: None
"""
print(f"{self._estimation_method} estimation measure")
@abstractmethod
[docs]
def compute_estimation(self, img_left: xr.Dataset, img_right: xr.Dataset) -> tuple[dict, dict, np.ndarray, dict]:
"""
Compute the phase cross correlation method
:param img_left: xarray.Dataset containing :
- im : 2D (row, col) xarray.DataArray
:param img_right: xarray.Dataset containing :
- im : 2D (row, col) xarray.DataArray
:return:row disparity: Dict
col disparity: Dict
Calculated shifts: list
Extra information about estimation : dict
"""
@staticmethod
[docs]
def update_cfg_with_estimation(
cfg: dict, disp_col: dict, disp_row: dict, shifts: np.ndarray, extra_dict: dict = None
) -> dict:
"""
Save calculated shifts in a configuration dictionary
:param cfg: user configuration
:param disp_col: dict with init and range for disparity in column
:param disp_row: dict with init and range for disparity in row
:param shifts: computed global shifts between left and right
:param extra_dict: Dictionary containing extra information about estimation
:return: cfg: global configuration
"""
cfg["input"]["col_disparity"] = disp_col
cfg["input"]["row_disparity"] = disp_row
cfg["pipeline"]["estimation"]["estimated_shifts"] = shifts.tolist()
if extra_dict is not None:
for key, value in extra_dict.items():
cfg["pipeline"]["estimation"][key] = value
return cfg