#!/usr/bin/env python
# coding: utf8
#
# Copyright (c) 2024 Centre National d'Etudes Spatiales (CNES).
#
# This file is part of PANDORA
#
# https://github.com/CNES/Pandora_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 to estimate confidence.
"""
from abc import ABCMeta, abstractmethod
from typing import Tuple
import xarray as xr
import numpy as np
[docs]
class AbstractCostVolumeConfidence:
"""
Abstract Cost Volume Confidence class
"""
[docs]
confidence_methods_avail = {}
def __new__(cls, **cfg: dict):
"""
Return the plugin associated with the confidence_method given in the configuration
:param cfg: the configuration {'confidence_method': value}
:type cfg: dictionary
"""
if cls is AbstractCostVolumeConfidence:
if isinstance(cfg["confidence_method"], str):
try:
return super(AbstractCostVolumeConfidence, cls).__new__(
cls.confidence_methods_avail[cfg["confidence_method"]]
)
except:
raise KeyError("No confidence method named {} supported".format(cfg["confidence_method"]))
else:
return super(AbstractCostVolumeConfidence, 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.confidence_methods_avail[short_name] = subclass
return subclass
return decorator
@abstractmethod
[docs]
def desc(self):
"""
Describes the confidence method
"""
print("Confidence method description")
@abstractmethod
[docs]
def confidence_prediction(
self,
disp: xr.Dataset,
img_left: xr.Dataset,
img_right: xr.Dataset,
cv: xr.Dataset,
) -> Tuple[xr.Dataset, xr.Dataset]:
"""
Computes a confidence prediction.
:param disp: the disparity map dataset or None
:type disp: xarray.Dataset or None
:param img_left: left Dataset image
:tye img_left: xarray.Dataset
:param img_right: right Dataset image
:type img_right: xarray.Dataset
:param cv: cost volume dataset
:type cv: xarray.Dataset
:return: None
"""
@staticmethod
[docs]
def allocate_confidence_map(
name_confidence_measure: str,
confidence_map: np.ndarray,
disp: xr.Dataset,
cv: xr.Dataset,
) -> Tuple[xr.Dataset, xr.Dataset]:
"""
Create or update the confidence measure : confidence_measure (xarray.DataArray of the cost volume and the
disparity map) by adding an indicator
:param name_confidence_measure: the name of the new confidence indicator
:type name_confidence_measure: string
:param confidence_map: the confidence map
:type confidence_map: 2D np.ndarray (row, col) dtype=np.float32
:param disp: the disparity map dataset or None
:type disp: xarray.Dataset or None
:param cv: cost volume dataset
:type cv: xarray.Dataset
:return: the disparity map and the cost volume with updated confidence measure
:rtype:
Tuple(xarray.Dataset, xarray.Dataset) with the data variables:
- confidence_measure 3D xarray.DataArray (row, col, indicator)
"""
# Add common prefix to confidence measure name
name_confidence_measure = "confidence_from_" + name_confidence_measure
if cv is not None:
# cost volume already contains a confidence map, it must be updated
if "confidence_measure" in cv.data_vars:
nb_row, nb_col, nb_indicator = cv["confidence_measure"].shape
# Add a new indicator to the confidence measure DataArray
conf_measure = np.full((nb_row, nb_col, nb_indicator + 1), np.nan, dtype=np.float32)
# old confidence measures
conf_measure[:, :, :-1] = cv["confidence_measure"].data
# new confidence measure
conf_measure[:, :, -1] = confidence_map
indicator = np.copy(cv.coords["indicator"])
indicator = np.append(indicator, name_confidence_measure)
# Remove confidence_measure dataArray from the dataset to update it
cv = cv.drop_dims("indicator")
coords_confidence_measure = [
cv.coords["row"],
cv.coords["col"],
indicator,
]
cv["confidence_measure"] = xr.DataArray(
data=conf_measure,
coords=coords_confidence_measure,
dims=["row", "col", "indicator"],
)
# Allocate the confidence measure in the cost volume Dataset
else:
coords_confidence_measure = [
cv.coords["row"],
cv.coords["col"],
[name_confidence_measure],
]
cv["confidence_measure"] = xr.DataArray(
data=confidence_map[:, :, np.newaxis].astype(np.float32),
coords=coords_confidence_measure,
dims=["row", "col", "indicator"],
)
if disp is not None:
# disparity already contains a confidence map, it must be updated
if "confidence_measure" in disp.data_vars:
nb_row, nb_col, nb_indicator = disp["confidence_measure"].shape
# Add a new indicator to the confidence measure DataArray
conf_measure = np.full((nb_row, nb_col, nb_indicator + 1), np.nan, dtype=np.float32)
# old confidence measures
conf_measure[:, :, :-1] = disp["confidence_measure"].data
# new confidence measure
conf_measure[:, :, -1] = confidence_map
indicator = np.copy(disp.coords["indicator"])
indicator = np.append(indicator, name_confidence_measure)
# Remove confidence_measure dataArray from the dataset to update it
disp = disp.drop_dims("indicator")
coords_confidence_measure = [
disp.coords["row"],
disp.coords["col"],
indicator,
]
disp["confidence_measure"] = xr.DataArray(
data=conf_measure,
coords=coords_confidence_measure,
dims=["row", "col", "indicator"],
)
else:
if cv is not None:
disp["confidence_measure"] = cv["confidence_measure"]
else:
coords_confidence_measure = [
disp.coords["row"],
disp.coords["col"],
[name_confidence_measure],
]
disp["confidence_measure"] = xr.DataArray(
data=confidence_map[:, :, np.newaxis].astype(np.float32),
coords=coords_confidence_measure,
dims=["row", "col", "indicator"],
)
return disp, cv