#!/usr/bin/env python
# coding: utf8
#
# Copyright (c) 2026 Centre National d'Etudes Spatiales (CNES).
#
# This file is part of PANDORA
#
# https://github.com/CNES/Pandora
#
# 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 classes and functions associated to the semantic segmentation step.
"""
from abc import ABCMeta, abstractmethod
from typing import Dict
import xarray as xr
[docs]
class AbstractSemanticSegmentation:
"""
Abstract SemanticSegmentation class
"""
[docs]
segmentation_methods_avail: Dict = {}
def __new__(cls, _img: xr.Dataset, **cfg: Dict[str, dict]):
"""
Return the plugin associated with the segmentation_method given in the configuration
:param img: xarray.Dataset of left image
:type img: xarray.Dataset
:param cfg: configuration {'segmentation_method': value}
:type cfg: dictionary
"""
if cls is AbstractSemanticSegmentation:
if isinstance(cfg["segmentation_method"], str):
try:
return super(AbstractSemanticSegmentation, cls).__new__(
cls.segmentation_methods_avail[cfg["segmentation_method"]]
)
except:
raise KeyError(
"No semantic segmentation method named {} supported".format(cfg["segmentation_method"])
)
else:
if isinstance(cfg["segmentation_method"], unicode): # type: ignore # pylint: disable=undefined-variable
# creating a plugin from registered short name given as unicode (py2 & 3 compatibility)
try:
return super(AbstractSemanticSegmentation, cls).__new__(
cls.segmentation_methods_avail[cfg["segmentation_method"].encode("utf-8")]
)
except:
raise KeyError(
"No semantic segmentation method named {} supported".format(cfg["segmentation_method"])
)
else:
return super(AbstractSemanticSegmentation, cls).__new__(cls)
return None
@classmethod
[docs]
def register_subclass(cls, short_name: str):
"""
Allows to register the subclass with its short name
:param short_name: the subclass to be registered
:type short_name: string
"""
def decorator(subclass):
"""
Registers the subclass in the available methods
:param subclass: the subclass to be registered
:type subclass: object
"""
cls.segmentation_methods_avail[short_name] = subclass
return subclass
return decorator
@abstractmethod
[docs]
def desc(self) -> None:
"""
Describes the semantic segmentation method
:return: None
"""
print("Semantic segmentation method description")
@abstractmethod
[docs]
def compute_semantic_segmentation(self, cv: xr.Dataset, img_left: xr.Dataset, img_right: xr.Dataset) -> xr.Dataset:
"""
Compute semantic segmentation
:param cv: the cost volume, with the data variables:
- cost_volume 3D xarray.DataArray (row, col, disp)
- confidence_measure (optional): 3D xarray.DataArray (row, col, indicator)
:type cv: xarray.Dataset
:param img_left: left Dataset image containing :
- im: 3D (band_im, row, col) xarray.DataArray float32
- disparity (optional): 3D (disp, row, col) xarray.DataArray float32
- msk (optional): 2D (row, col) xarray.DataArray int16
- classif (optional): 3D (band_classif, row, col) xarray.DataArray int16
- segm (optional): 2D (row, col) xarray.DataArray int16
- edges (optional): 2D (row, col) xarray.DataArray int16
:type img_left: xarray
:param img_right: right Dataset image containing :
- im: 3D (band_im, row, col) xarray.DataArray float32
- disparity (optional): 3D (disp, row, col) xarray.DataArray float32
- msk (optional): 2D (row, col) xarray.DataArray int16
- classif (optional): 3D (band_classif, row, col) xarray.DataArray int16
- segm (optional): 2D (row, col) xarray.DataArray int16
- edges (optional): 2D (row, col) xarray.DataArray int16
:type img_right: xarray
:return: The semantic segmentation in the left image dataset with the data variables:
- im: 3D (band_im, row, col) xarray.DataArray float32
- disparity (optional): 3D (disp, row, col) xarray.DataArray float32
- msk (optional): 2D (row, col) xarray.DataArray int16
- classif (optional): 3D (band_classif, row, col) xarray.DataArray int16
- segm (optional): 2D (row, col) xarray.DataArray int16
- edges (optional): 2D (row, col) xarray.DataArray int16
- initial : 2D (row, col) xarray.DataArray semantic segmentation
:rtype: xarray.Dataset
"""