#!/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 functions for estimating confidence from image.
"""
from typing import Dict, Tuple
import numpy as np
from json_checker import Checker, And
import xarray as xr
from pandora.img_tools import compute_std_raster
from . import cost_volume_confidence
@cost_volume_confidence.AbstractCostVolumeConfidence.register_subclass("std_intensity")
[docs]
class StdIntensity(cost_volume_confidence.AbstractCostVolumeConfidence):
"""
StdIntensity class allows to estimate a confidence measure from the left image by calculating the standard
deviation of the intensity
"""
# Method name
[docs]
_method = "intensity_std"
# Indicator
def __init__(self, **cfg: str) -> None:
"""
:param cfg: optional configuration, {'confidence_method': 'std_intensity'}
:type cfg: dict
:return: None
"""
self.cfg = self.check_conf(**cfg)
# Indicator
self._indicator = self._method + self.cfg["indicator"]
[docs]
def check_conf(self, **cfg: str) -> Dict[str, str]:
"""
Add default values to the dictionary if there are missing elements and check if the dictionary is correct
:param cfg: std_intensity configuration
:type cfg: dict
:return cfg: std_intensity configuration updated
:rtype: dict
"""
if "indicator" not in cfg:
cfg["indicator"] = self._indicator
schema = {"confidence_method": And(str, lambda input: "std_intensity"), "indicator": str}
checker = Checker(schema)
checker.validate(cfg)
return cfg
[docs]
def desc(self) -> None:
"""
Describes the confidence method
:return: None
"""
print("Intensity confidence method")
[docs]
def confidence_prediction(
self,
disp: xr.Dataset,
img_left: xr.Dataset = None,
img_right: xr.Dataset = None,
cv: xr.Dataset = None,
) -> Tuple[xr.Dataset, xr.Dataset]:
"""
Computes a confidence measure that evaluates the standard deviation of intensity of the left image
:param disp: the disparity map dataset
:type disp: xarray.Dataset
: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: the disparity map and the cost volume with a new indicator 'ambiguity_confidence' in the DataArray
confidence_measure
:rtype: Tuple(xarray.Dataset, xarray.Dataset) with the data variables:
- confidence_measure 3D xarray.DataArray (row, col, indicator)
"""
nb_row, nb_col = img_left.sizes["row"], img_left.sizes["col"]
band = cv.attrs["band_correl"]
window_size = cv.attrs["window_size"]
confidence_measure = np.full((nb_row, nb_col), np.nan, dtype=np.float32)
offset_row_col = int((window_size - 1) / 2)
if offset_row_col != 0:
confidence_measure[offset_row_col:-offset_row_col, offset_row_col:-offset_row_col] = compute_std_raster(
img_left, window_size, band
)
else:
confidence_measure = compute_std_raster(img_left, window_size, band)
disp, cv = self.allocate_confidence_map(self._indicator, confidence_measure, disp, cv)
return disp, cv