# -*- coding: utf-8 -*-
# Copyright 2023, SERTIT-ICube - France, https://sertit.unistra.fr/
# This file is part of eoreader project
# https://github.com/sertit/eoreader
#
# 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.
"""
DIMAP V2 super class.
See `here <www.engesat.com.br/wp-content/uploads/PleiadesUserGuide-17062019.pdf>`_
for more information.
"""
import logging
import time
from abc import abstractmethod
from datetime import date, datetime
from enum import unique
from pathlib import Path
from typing import Union
import affine
import geopandas as gpd
import numpy as np
import pandas as pd
import rasterio
import xarray as xr
from cloudpathlib import CloudPath
from lxml import etree
from rasterio import crs as riocrs
from rasterio import features, transform
from sertit import files, geometry, rasters_rio, vectors
from sertit.misc import ListEnum
from sertit.vectors import WGS84
from shapely.geometry import Polygon, box
from eoreader import DATETIME_FMT, EOREADER_NAME, cache, utils
from eoreader.bands import (
ALL_CLOUDS,
BLUE,
CA,
CIRRUS,
CLOUDS,
GREEN,
NARROW_NIR,
NIR,
PAN,
RAW_CLOUDS,
RED,
SHADOWS,
VRE_1,
VRE_2,
VRE_3,
BandNames,
to_str,
)
from eoreader.exceptions import InvalidProductError, InvalidTypeError
from eoreader.products import VhrProduct
from eoreader.products.optical.optical_product import RawUnits
from eoreader.reader import Constellation
from eoreader.utils import simplify
LOGGER = logging.getLogger(EOREADER_NAME)
_DIMAP_BAND_MTD = {
PAN: "P",
BLUE: "B0",
GREEN: "B1",
RED: "B2",
NIR: "B3",
NARROW_NIR: "B3",
}
_PNEO_BAND_MTD = {
PAN: "P",
BLUE: "B",
GREEN: "G",
RED: "R",
NIR: "NIR",
NARROW_NIR: "NIR",
VRE_1: "RE",
VRE_2: "RE",
VRE_3: "RE",
CA: "DB", # deep blue
}
[docs]@unique
class DimapV2ProductType(ListEnum):
"""
DIMAP V2 product types (processing level).
See `here <www.engesat.com.br/wp-content/uploads/PleiadesUserGuide-17062019.pdf>`_
(A.1.1.2 Variable Key Information) for more information.
"""
SEN = "Primary"
"""
Primary (L1A), abbreviation for Sensor.
The Primary product is the geometric processing level closest to the natural image acquired by the sensor.
"""
PRJ = "Projected"
"""
Projected (L2A).
Compared to Primary level, the projected level results from an additional process to map the image
onto an Earth cartographic system at a fixed altitude value.
The image is georeferenced without correction from acquisition and terrain off-nadir effects.
This image-to-map transformation is directly compatible with GIS environment,
for example to overlay the image on other data.
"""
ORT = "Ortho Single Image"
"""
Ortho (L3), single image.
The Ortho product is a georeferenced image in Earth geometry,
corrected from acquisition and terrain off-nadir effects.
The Ortho is produced as a standard, with fully automatic processing.
"""
MOS = "Ortho Mosaic Image"
"""
Ortho (L3), mosaic image.
The Ortho product is a georeferenced image in Earth geometry,
corrected from acquisition and terrain off-nadir effects.
The Ortho is produced as a standard, with fully automatic processing.
"""
[docs]@unique
class DimapV2RadiometricProcessing(ListEnum):
"""
DIMAP V2 radiometric processing.
See `here <https://engesat.com.br/wp-content/uploads/PleiadesUserGuide-17062019.pdf>`_
(Paragraph 2.4) for more information.
"""
BASIC = "BASIC"
"""
In the BASIC radiometric option, the imagery values are digital numbers (DN)
quantifying the energy recorded by the detector corrected relative
to the other detectors to avoid non-uniformity noise.
"""
REFLECTANCE = "REFLECTANCE"
"""
In the REFLECTANCE radiometric option, the imagery values are corrected
from radiometric sensor calibration and systematic effects of the atmosphere
(molecular or Rayleigh diffusion and given in reflectance physical unit).
"""
LINEAR_STRETCH = "LINEAR_STRETCH"
"""
Relates to the BASIC option at 8-bit depth.
"""
SEAMLESS = "SEAMLESS"
"""
Relates to the mosaic option.
In this case, the spectral properties cannot be retrieved since the initial images have undergone several radiometric adjustments for aesthetic rendering.
"""
[docs]@unique
class DimapV2BandCombination(ListEnum):
"""
DIMAP V2 products band combination
See `here <www.engesat.com.br/wp-content/uploads/PleiadesUserGuide-17062019.pdf>`_
(A.1.1.2 Variable Key Information) for more information.
"""
P = "Panchromatic"
"""
The Pléiades Panchromatic product includes only one black and white band.
It covers wavelengths between 0.47 and 0.83 μm of the visible spectrum.
The product pixel size is 0.5 m (Ortho).
"""
MS = "Multi Spectral"
"""
The Multispectral product includes four Multispectral (colour) bands: blue, red, green and near infrared.
The product pixel size is 2 m (Ortho).
"""
MS_N = "Multi Spectral in natural color"
"""
The Multispectral product includes four Multispectral (colour) bands: blue, red, green and near infrared.
The product pixel size is 2 m (Ortho).
(3 bands: BLUE GREEN RED)
"""
MS_X = "Multi Spectral in false color"
"""
The Multispectral product includes four Multispectral (colour) bands: blue, red, green and near infrared.
The product pixel size is 2 m (Ortho).
(3 bands: GREEN RED NIR)
"""
MS_FS = "Multi Spectral Full"
"""
Full MS: Multispectral (6 bands).
Only Pleiades-Neo
"""
PMS = "Pansharpened Multi Spectral"
"""
Pan-sharpened products combine the visual coloured information of the Multispectral data with the details
provided by of the Panchromatic data, resulting in a higher resolution 0.5 m colour product (4 bands)
"""
PMS_N = "Pansharpened Multi Spectral in natural color"
"""
Pan-sharpened products combine the visual coloured information of the Multispectral data with the details
provided by of the Panchromatic data, resulting in a higher resolution 0.5 m colour product
(3 bands: BLUE GREEN RED)
"""
PMS_X = "Pansharpened Multi Spectral in false color"
"""
Pan-sharpened products combine the visual coloured information of the Multispectral data with the details
provided by of the Panchromatic data, resulting in a higher resolution 0.5 m colour product
(3 bands: GREEN RED NIR)
"""
PMS_FS = "Pansharpened Multi Spectral Full"
"""
Full PMS: Pansharpening (6 bands).
Only Pleiades-Neo
"""
[docs]class DimapV2Product(VhrProduct):
"""
Super Class of DIMAP V2 products.
See `here <www.engesat.com.br/wp-content/uploads/PleiadesUserGuide-17062019.pdf>`_
for more information.
"""
[docs] def __init__(
self,
product_path: Union[str, CloudPath, Path],
archive_path: Union[str, CloudPath, Path] = None,
output_path: Union[str, CloudPath, Path] = None,
remove_tmp: bool = False,
**kwargs,
) -> None:
self._empty_mask = []
self._altitude = None
# Initialization from the super class
super().__init__(product_path, archive_path, output_path, remove_tmp, **kwargs)
def _pre_init(self, **kwargs) -> None:
"""
Function used to pre_init the products
(setting needs_extraction and so on)
"""
self._has_cloud_cover = True
self.needs_extraction = False
self._proj_prod_type = [DimapV2ProductType.SEN, DimapV2ProductType.PRJ]
# Raw units
root, _ = self.read_mtd()
rad_proc = DimapV2RadiometricProcessing.from_value(
root.findtext(".//RADIOMETRIC_PROCESSING")
)
if rad_proc == DimapV2RadiometricProcessing.REFLECTANCE:
self._raw_units = RawUnits.REFL
elif rad_proc in [
DimapV2RadiometricProcessing.BASIC,
DimapV2RadiometricProcessing.LINEAR_STRETCH,
]:
self._raw_units = RawUnits.DN
else:
self._raw_units = RawUnits.NONE
# Post init done by the super class
super()._pre_init(**kwargs)
def _post_init(self, **kwargs) -> None:
"""
Function used to post_init the products
(setting sensor type, band names and so on)
"""
# Band combination
root, _ = self.read_mtd()
band_combi = root.findtext(".//SPECTRAL_PROCESSING")
if not band_combi:
raise InvalidProductError(
"Cannot find the band combination (from SPECTRAL_PROCESSING) type in the metadata file"
)
self.band_combi = getattr(DimapV2BandCombination, band_combi.replace("-", "_"))
# Post init done by the super class
super()._post_init(**kwargs)
def _set_pixel_size(self) -> None:
"""
Set product default pixel size (in meters)
"""
# Not Pansharpened images
if self.band_combi in [
DimapV2BandCombination.MS,
DimapV2BandCombination.MS_X,
DimapV2BandCombination.MS_N,
DimapV2BandCombination.MS_FS,
]:
self.pixel_size = self._ms_res
# Pansharpened images
else:
self.pixel_size = self._pan_res
def _map_bands_core(self, **kwargs) -> None:
"""
Map bands
"""
# Open spectral bands
pan = kwargs.get("pan")
blue = kwargs.get("blue")
green = kwargs.get("green")
red = kwargs.get("red")
nir = kwargs.get("nir")
ca = kwargs.get("ca")
vre = kwargs.get("vre")
# Manage bands of the product
if self.band_combi == DimapV2BandCombination.P:
self.bands.map_bands({PAN: pan.update(id=1)})
elif self.band_combi == DimapV2BandCombination.MS:
self.bands.map_bands(
{
BLUE: blue.update(id=3),
GREEN: green.update(id=2),
RED: red.update(id=1),
NARROW_NIR: nir.update(id=4),
NIR: nir.update(id=4),
}
)
elif self.band_combi == DimapV2BandCombination.PMS:
self.bands.map_bands(
{
BLUE: blue.update(id=3, gsd=self._pan_res),
GREEN: green.update(id=2, gsd=self._pan_res),
RED: red.update(id=1, gsd=self._pan_res),
NARROW_NIR: nir.update(id=4, gsd=self._pan_res),
NIR: nir.update(id=4, gsd=self._pan_res),
}
)
elif self.band_combi == DimapV2BandCombination.MS_N:
self.bands.map_bands(
{
BLUE: blue.update(id=3),
GREEN: green.update(id=2),
RED: red.update(id=1),
}
)
elif self.band_combi == DimapV2BandCombination.PMS_N:
self.bands.map_bands(
{
BLUE: blue.update(id=3, gsd=self._pan_res),
GREEN: green.update(id=2, gsd=self._pan_res),
RED: red.update(id=1, gsd=self._pan_res),
}
)
elif self.band_combi == DimapV2BandCombination.MS_X:
self.bands.map_bands(
{
GREEN: green.update(id=1),
RED: red.update(id=2),
NIR: nir.update(id=3),
NARROW_NIR: nir.update(id=3),
}
)
elif self.band_combi == DimapV2BandCombination.PMS_X:
self.bands.map_bands(
{
GREEN: green.update(id=1, gsd=self._pan_res),
RED: red.update(id=2, gsd=self._pan_res),
NIR: nir.update(id=3, gsd=self._pan_res),
NARROW_NIR: nir.update(id=3, gsd=self._pan_res),
}
)
elif self.band_combi == DimapV2BandCombination.MS_FS:
# Only PLD-Neo
self.bands.map_bands(
{
BLUE: blue.update(id=3),
GREEN: green.update(id=2),
RED: red.update(id=1),
NIR: nir.update(id=4),
NARROW_NIR: nir.update(id=4),
VRE_1: vre.update(id=5),
VRE_2: vre.update(id=5),
VRE_3: vre.update(id=5),
CA: ca.update(id=6),
}
)
elif self.band_combi == DimapV2BandCombination.PMS_FS:
# Only PLD-Neo
self.bands.map_bands(
{
BLUE: blue.update(id=3, gsd=self._pan_res),
GREEN: green.update(id=2, gsd=self._pan_res),
RED: red.update(id=1, gsd=self._pan_res),
NIR: nir.update(id=4, gsd=self._pan_res),
NARROW_NIR: nir.update(id=4, gsd=self._pan_res),
VRE_1: vre.update(id=5, gsd=self._pan_res),
VRE_2: vre.update(id=5, gsd=self._pan_res),
VRE_3: vre.update(id=5, gsd=self._pan_res),
CA: ca.update(id=6, gsd=self._pan_res),
}
)
else:
raise InvalidProductError(
f"Unusual band combination: {self.band_combi.name}"
)
def _set_product_type(self) -> None:
"""Set products type"""
# get product type
prod_type = self.split_name[3]
self.product_type = getattr(DimapV2ProductType, prod_type)
# Manage not orthorectified product
if self.product_type in [DimapV2ProductType.SEN, DimapV2ProductType.PRJ]:
self.is_ortho = False
def _get_raw_crs(self) -> riocrs.CRS:
"""
Get raw CRS of the tile
Returns:
rasterio.crs.CRS: CRS object
"""
# Open metadata
root, _ = self.read_mtd()
# Get CRS
crs_name = root.findtext(".//GEODETIC_CRS_CODE")
if not crs_name:
crs_name = root.findtext(".//PROJECTED_CRS_CODE")
if not crs_name:
raise InvalidProductError(
"Cannot find the CRS name (from GEODETIC_CRS_NAME or PROJECTED_CRS_CODE) type in the metadata file"
)
return riocrs.CRS.from_string(crs_name)
[docs] @cache
def crs(self) -> riocrs.CRS:
"""
Get UTM projection of the tile
.. code-block:: python
>>> from eoreader.reader import Reader
>>> path = r"IMG_PHR1B_PMS_001"
>>> prod = Reader().open(path)
>>> prod.crs()
CRS.from_epsg(32618)
Returns:
rasterio.crs.CRS: CRS object
"""
raw_crs = self._get_raw_crs()
if raw_crs.is_projected:
utm = raw_crs
else:
# Open metadata
root, _ = self.read_mtd()
# Open the Bounding_Polygon
vertices = list(root.iterfind(".//Vertex"))
# Get the mean lon lat
lon = float(np.mean([float(v.findtext("LON")) for v in vertices]))
lat = float(np.mean([float(v.findtext("LAT")) for v in vertices]))
# Compute UTM crs from center long/lat
utm = vectors.to_utm_crs(lon, lat)
return utm
[docs] @cache
def extent(self, **kwargs) -> gpd.GeoDataFrame:
"""
Get UTM extent of the tile.
Returns:
gpd.GeoDataFrame: Extent in UTM
"""
# Get MTD XML file
root, _ = self.read_mtd()
# Compute extent corners
corners = [
[float(vertex.findtext("LON")), float(vertex.findtext("LAT"))]
for vertex in root.iterfind(".//Dataset_Extent/Vertex")
]
extent_wgs84 = gpd.GeoDataFrame(
geometry=[Polygon(corners)],
crs=vectors.WGS84,
)
# Not square extent
utm_extent_raw = extent_wgs84.to_crs(self.crs())
utm_extent = gpd.GeoDataFrame(
geometry=[box(*utm_extent_raw.total_bounds)],
crs=self.crs(),
)
return utm_extent
[docs] def get_datetime(self, as_datetime: bool = False) -> Union[str, datetime]:
"""
Get the product's acquisition datetime, with format :code:`YYYYMMDDTHHMMSS` <-> :code:`%Y%m%dT%H%M%S`
.. code-block:: python
>>> from eoreader.reader import Reader
>>> path = r"IMG_PHR1B_PMS_001"
>>> prod = Reader().open(path)
>>> prod.get_datetime(as_datetime=True)
datetime.datetime(2020, 5, 11, 2, 31, 58)
>>> prod.get_datetime(as_datetime=False)
'20200511T023158'
Args:
as_datetime (bool): Return the date as a datetime.datetime. If false, returns a string.
Returns:
Union[str, datetime.datetime]: Its acquisition datetime
"""
if self.datetime is None:
# Get MTD XML file
root, _ = self.read_mtd()
date_str = root.findtext(".//IMAGING_DATE")
time_str = root.findtext(".//IMAGING_TIME")
if not date_str or not time_str:
raise InvalidProductError(
"Cannot find the product imaging date and time in the metadata file."
)
# Convert to datetime
date_dt = date.fromisoformat(date_str)
try:
time_dt = time.strptime(time_str, "%H:%M:%S.%fZ")
except ValueError:
try:
time_dt = time.strptime(
time_str, "%H:%M:%S.%f"
) # Sometimes without a Z
except ValueError:
time_dt = time.strptime(
time_str, "%H:%M:%S"
) # Sometimes without microseconds
date_str = (
f"{date_dt.strftime('%Y%m%d')}T{time.strftime('%H%M%S', time_dt)}"
)
if as_datetime:
date_str = datetime.strptime(date_str, DATETIME_FMT)
else:
date_str = self.datetime
if not as_datetime:
date_str = date_str.strftime(DATETIME_FMT)
return date_str
def _get_name_constellation_specific(self) -> str:
"""
Set product real name from metadata
Returns:
str: True name of the product (from metadata)
"""
return files.get_filename(self._get_tile_path()).replace("DIM_", "")
def _manage_invalid_pixels(
self, band_arr: xr.DataArray, band: BandNames, **kwargs
) -> xr.DataArray:
"""
Manage invalid pixels (Nodata, saturated, defective...)
Args:
band_arr (xr.DataArray): Band array
band (BandNames): Band name as a SpectralBandNames
kwargs: Other arguments used to load bands
Returns:
xr.DataArray: Cleaned band array
"""
# array data
width = band_arr.rio.width
height = band_arr.rio.height
vec_tr = transform.from_bounds(
*band_arr.rio.bounds(), band_arr.rio.width, band_arr.rio.height
)
# Get detector footprint to deduce the outside nodata
nodata = self._load_nodata(width, height, vec_tr, **kwargs)
# Load masks and merge them into the nodata
try:
# Out of order detectors
nodata_vec = self.open_mask("DET", **kwargs)
# Hidden area vector mask
nodata_vec = pd.concat([nodata_vec, self.open_mask("VIS", **kwargs)])
# # Straylight vector mask
# nodata_vec = pd.concat(
# [nodata_vec, self.open_mask("SLT", **kwargs)]
# )
if len(nodata_vec) > 0:
# Rasterize mask
mask = features.rasterize(
nodata_vec.geometry,
out_shape=(height, width),
fill=self._mask_false, # Outside vector
default_value=self._mask_true, # Inside vector
transform=vec_tr,
dtype=np.uint8,
)
nodata = nodata | mask
except InvalidProductError:
pass
return self._set_nodata_mask(band_arr, nodata)
def _to_reflectance(
self,
band_arr: xr.DataArray,
path: Union[Path, CloudPath],
band: BandNames,
**kwargs,
) -> xr.DataArray:
"""
Converts band to reflectance
See
`here <https://www.intelligence-airbusds.com/automne/api/docs/v1.0/document/download/ZG9jdXRoZXF1ZS1kb2N1bWVudC01NTY0Mw==/ZG9jdXRoZXF1ZS1maWxlLTU1NjQy/airbus-pleiades-imagery-user-guide-15042021.pdf>`_
(Appendix D)
Args:
band_arr (xr.DataArray):
path (Union[Path, CloudPath]):
band (BandNames):
**kwargs: Other keywords
Returns:
xr.DataArray: Band in reflectance
"""
if self._raw_units == RawUnits.REFL:
# Compute the correct radiometry of the band
original_dtype = band_arr.encoding["dtype"]
if original_dtype == "uint16":
band_arr /= 10000.0
elif self._raw_units == RawUnits.DN:
# Convert DN into radiance
band_arr = self._dn_to_toa_rad(band_arr, band)
# Convert radiance into reflectance
band_arr = self._toa_rad_to_toa_refl(band_arr, band)
else:
LOGGER.warning(
"The spectral properties of a SEAMLESS radiometric processed image "
"cannot be retrieved since the initial images have undergone "
"several radiometric adjustments for aesthetic rendering."
"Returned as is."
)
# To float32
if band_arr.dtype != np.float32:
band_arr = band_arr.astype(np.float32)
return band_arr
def _manage_nodata(
self, band_arr: xr.DataArray, band: BandNames, **kwargs
) -> xr.DataArray:
"""
Manage only nodata pixels
Args:
band_arr (xr.DataArray): Band array
band (BandNames): Band name as an SpectralBandNames
kwargs: Other arguments used to load bands
Returns:
xr.DataArray: Cleaned band array
"""
# array data
width = band_arr.rio.width
height = band_arr.rio.height
vec_tr = transform.from_bounds(
*band_arr.rio.bounds(), band_arr.rio.width, band_arr.rio.height
)
# Get detector footprint to deduce the outside nodata
nodata = self._load_nodata(width, height, vec_tr, **kwargs)
return self._set_nodata_mask(band_arr, nodata)
[docs] @cache
def get_mean_sun_angles(self) -> (float, float):
"""
Get Mean Sun angles (Azimuth and Zenith angles)
.. code-block:: python
>>> from eoreader.reader import Reader
>>> path = r"IMG_PHR1A_PMS_001"
>>> prod = Reader().open(path)
>>> prod.get_mean_sun_angles()
(45.6624568841367, 30.219881316357643)
Returns:
(float, float): Mean Azimuth and Zenith angle
"""
# Get MTD XML file
root, _ = self.read_mtd()
# Open zenith and azimuth angle
try:
center_vals = [
a
for a in root.iterfind(".//Located_Geometric_Values")
if a.findtext("LOCATION_TYPE") == "Center"
][0]
elev_angle = float(center_vals.findtext(".//SUN_ELEVATION"))
azimuth_angle = float(center_vals.findtext(".//SUN_AZIMUTH"))
except TypeError:
raise InvalidProductError("Azimuth or Zenith angles not found in metadata!")
# From elevation to zenith
zenith_angle = 90.0 - elev_angle
return azimuth_angle, zenith_angle
[docs] @cache
def get_mean_viewing_angles(self) -> (float, float, float):
"""
Get Mean Viewing angles (azimuth, off-nadir and incidence angles)
.. code-block:: python
>>> from eoreader.reader import Reader
>>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip"
>>> prod = Reader().open(path)
>>> prod.get_mean_viewing_angles()
Returns:
(float, float, float): Mean azimuth, off-nadir and incidence angles
"""
def incidence_to_off_nadir(inc_angle: float, orbit_h: float = 695000) -> float:
earth_radius = 6378137
orbit_coeff = (earth_radius + orbit_h) / earth_radius
return np.rad2deg(
np.arcsin(np.sin(np.deg2rad(90 - inc_angle)) / orbit_coeff)
)
# Get MTD XML file
root, _ = self.read_mtd()
# Open zenith and azimuth angle
try:
center_vals = [
a
for a in root.iterfind(".//Located_Geometric_Values")
if a.findtext("LOCATION_TYPE") == "Center"
][0]
incidence_angle = float(center_vals.findtext(".//INCIDENCE_ANGLE"))
az = float(center_vals.findtext(".//AZIMUTH_ANGLE"))
except TypeError:
raise InvalidProductError("Azimuth or Zenith angles not found in metadata!")
# Compute off nadir angles
off_nadir = incidence_to_off_nadir(
inc_angle=incidence_angle, orbit_h=self._altitude
)
return az, off_nadir, incidence_angle
@cache
def _read_mtd(self) -> (etree._Element, dict):
"""
Read metadata and outputs the metadata XML root and its namespaces as a dict
Returns:
(etree._Element, dict): Metadata XML root and its namespaces as a dict
"""
mtd_from_path = "DIM_*.XML"
mtd_archived = r"DIM_.*\.XML"
return self._read_mtd_xml(mtd_from_path, mtd_archived)
def _has_cloud_band(self, band: BandNames) -> bool:
"""
Does this product has the specified cloud band ?
"""
if band in [CIRRUS, SHADOWS]:
has_band = False
else:
has_band = True
return has_band
def _open_clouds(
self,
bands: list,
pixel_size: float = None,
size: Union[list, tuple] = None,
**kwargs,
) -> dict:
"""
Load cloud files as xarrays.
Args:
bands (list): List of the wanted bands
pixel_size (int): Band pixel size in meters
size (Union[tuple, list]): Size of the array (width, height). Not used if pixel_size is provided.
kwargs: Additional arguments
Returns:
dict: Dictionary {band_name, band_xarray}
"""
band_dict = {}
if bands:
# Load cloud vector
cld_vec = self.open_mask("CLD", **kwargs)
has_vec = len(cld_vec) > 0
# Load default xarray as a template
def_utm_path = self._get_default_utm_band(pixel_size=pixel_size, size=size)
with rasterio.open(str(def_utm_path)) as dst:
if dst.count > 1:
def_xarr = utils.read(
dst,
pixel_size=pixel_size,
size=size,
indexes=[self.bands[self.get_default_band()].id],
)
else:
def_xarr = utils.read(dst, pixel_size=pixel_size, size=size)
# Load nodata
width = def_xarr.rio.width
height = def_xarr.rio.height
vec_tr = transform.from_bounds(
*def_xarr.rio.bounds(), def_xarr.rio.width, def_xarr.rio.height
)
nodata = self._load_nodata(width, height, vec_tr, **kwargs)
# Rasterize features if existing vector
if has_vec:
cld_arr = features.rasterize(
cld_vec.geometry,
out_shape=(height, width),
fill=self._mask_false, # Outside vector
default_value=self._mask_true, # Inside vector
transform=vec_tr,
dtype=np.uint8,
)
# Rasterize gives a 2D array, we want a 3D array
cld_arr = np.expand_dims(cld_arr, axis=0)
else:
cld_arr = np.zeros(
(1, def_xarr.rio.height, def_xarr.rio.width), dtype=np.uint8
)
for band in bands:
if band in [ALL_CLOUDS, CLOUDS, RAW_CLOUDS]:
cloud = self._create_mask(
def_xarr,
cld_arr,
nodata,
)
else:
raise InvalidTypeError(f"Non existing cloud band for: {band}")
# Rename
band_name = to_str(band)[0]
cloud.attrs["long_name"] = band_name
band_dict[band] = cloud.rename(band_name).astype(np.float32)
return band_dict
[docs] def open_mask(self, mask_str: str, **kwargs) -> gpd.GeoDataFrame:
"""
Open DIMAP V2 mask (GML files stored in MASKS) as :code:`gpd.GeoDataFrame`.
Masks than can be called that way are:
- :code:`CLD`: Cloud vector mask
- :code:`DET`: Out of order detectors vector mask
- :code:`QTE`: Synthetic technical quality vector mask
- :code:`ROI`: Region of Interest vector mask
- :code:`SLT`: Straylight vector mask
- :code:`SNW`: Snow vector mask
- :code:`VIS`: Hidden area vector mask (optional)
.. code-block:: python
>>> from eoreader.reader import Reader
>>> from eoreader.bands import *
>>> path = r"IMG_PHR1A_PMS_001"
>>> prod.open_mask("ROI")
gml_id ... geometry
0 source_image_footprint-DS_PHR1A_20200511023124... ... POLYGON ((118.86239 -2.81569, 118.86255 -2.815...
[1 rows x 3 columns]
Args:
mask_str (str): Mask name, such as CLD, DET, ROI...
Returns:
gpd.GeoDataFrame: Mask as a vector
"""
# Check inputs
mandatory_masks = ["CLD", "DET", "QTE", "ROI", "SLT", "SNW"]
optional_masks = ["VIS"]
assert mask_str in mandatory_masks + optional_masks
crs = self.crs()
mask_name = f"{self.condensed_name}_MSK_{mask_str}.geojson"
mask_path, mask_exists = self._get_out_path(mask_name)
if mask_exists:
mask = vectors.read(mask_path)
elif mask_str in self._empty_mask:
# Empty mask cannot be written on file
mask = gpd.GeoDataFrame(geometry=[], crs=crs)
else:
try:
if self.is_archived:
# Open the zip file
mask = vectors.read(
self.path,
archive_regex=rf".*MASKS.*{mask_str}.*\.GML",
crs=crs,
)
else:
mask_gml_path = files.get_file_in_dir(
self.path.joinpath("MASKS"),
f"*{mask_str}*.GML",
exact_name=True,
)
mask = vectors.read(mask_gml_path, crs=crs)
except FileNotFoundError:
if mask_str in optional_masks:
mask = gpd.GeoDataFrame(geometry=[], crs=crs)
else:
raise InvalidProductError(
f"Mask {mask_str} not found for {self.path.joinpath('MASKS')}"
)
# Convert mask to correct CRS
if not mask.empty and self.product_type in [
DimapV2ProductType.SEN,
DimapV2ProductType.PRJ,
]:
# Sometimes the GML mask lacks crs (why ?)
if not mask.crs:
mask.crs = self._get_raw_crs()
mask.crs = WGS84
LOGGER.info(f"Orthorectifying {mask_str}")
with rasterio.open(str(self._get_tile_path())) as dim_dst:
# Rasterize mask (no transform as we have the vector in image geometry)
LOGGER.debug(f"\tRasterizing {mask_str}")
mask_raster = features.rasterize(
mask.geometry,
out_shape=(dim_dst.height, dim_dst.width),
fill=self._mask_false, # Outside vector
default_value=self._mask_true, # Inside vector
dtype=np.uint8,
)
# Check mask validity (to avoid reprojecting)
# All null
if mask_raster.max() == 0:
mask = gpd.GeoDataFrame(geometry=[], crs=crs)
# All valid
elif mask_raster.min() == 1:
pass
else:
# Reproject mask raster
LOGGER.debug(f"\tReprojecting {mask_str}")
dem_path = self._get_dem_path(**kwargs)
reproj_data = self._reproject(
mask_raster, dim_dst.meta, dim_dst.rpcs, dem_path, **kwargs
)
# Vectorize mask raster
LOGGER.debug(f"\tRevectorizing {mask_str}")
mask = rasters_rio.vectorize(
reproj_data,
values=self._mask_true,
default_nodata=self._mask_false,
)
# Do not keep pixelized mask
mask = geometry.simplify_footprint(mask, self.pixel_size)
# Sometimes the GML mask lacks crs (why ?)
elif (
not mask.empty
and not mask.crs
and self.product_type
in [
DimapV2ProductType.ORT,
DimapV2ProductType.MOS,
]
):
# Convert to target CRS
mask.crs = self._get_raw_crs()
mask = mask.to_crs(self.crs())
# Save to file
if mask.empty:
# Empty mask cannot be written on file
self._empty_mask.append(mask_str)
else:
mask.to_file(str(mask_path), driver="GeoJSON")
return mask
def _load_nodata(
self, width: int, height: int, trf: affine.Affine, **kwargs
) -> Union[np.ndarray, None]:
"""
Load nodata (unimaged pixels) as a numpy array.
Args:
width (int): Array width
height (int): Array height
trf (affine.Affine): Transform to georeference array
Returns:
Union[np.ndarray, None]: Nodata array
"""
nodata_det = self.open_mask("ROI", **kwargs)
if all(nodata_det.is_empty):
return np.zeros((1, height, width), dtype=np.uint8)
else:
# Rasterize nodata
return features.rasterize(
nodata_det.geometry,
out_shape=(height, width),
fill=self._mask_true, # Outside ROI = nodata (inverted compared to the usual)
default_value=self._mask_false, # Inside ROI = not nodata
transform=trf,
dtype=np.uint8,
)
def _get_tile_path(self) -> Union[CloudPath, Path]:
"""
Get the DIMAP filepath
Returns:
Union[CloudPath, Path]: DIMAP filepath
"""
return self._get_path("DIM_", "XML")
def _dn_to_toa_rad(self, dn_arr: xr.DataArray, band: BandNames) -> xr.DataArray:
"""
Compute DN to TOA radiance
See
`here <https://www.intelligence-airbusds.com/automne/api/docs/v1.0/document/download/ZG9jdXRoZXF1ZS1kb2N1bWVudC01NTY0Mw==/ZG9jdXRoZXF1ZS1maWxlLTU1NjQy/airbus-pleiades-imagery-user-guide-15042021.pdf>`_
for more information. (Appendix D)
Args:
dn_arr (xr.DataArray): DN array
band (BandNames): Band
Returns:
xr.DataArray: TOA Radiance array
"""
if self.constellation == Constellation.PNEO:
band_mtd_str = _PNEO_BAND_MTD[band]
else:
band_mtd_str = _DIMAP_BAND_MTD[band]
# Get MTD XML file
root, _ = self.read_mtd()
# Convert DN to TOA radiance
# <MEASURE_DESC>Raw radiometric counts (DN) to TOA Radiance (L). Formulae L=DN/GAIN+BIAS</MEASURE_DESC>
try:
rad_gain = None
rad_bias = None
for br in root.iterfind(".//Band_Radiance"):
if br.findtext("BAND_ID") == band_mtd_str:
rad_gain = float(br.findtext("GAIN"))
rad_bias = float(br.findtext("BIAS"))
break
if rad_gain is None or rad_bias is None:
raise TypeError
except TypeError:
raise InvalidProductError(
"GAIN and BIAS from Band_Radiance not found in metadata!"
)
return dn_arr / rad_gain + rad_bias
def _toa_rad_to_toa_refl(
self, rad_arr: xr.DataArray, band: BandNames
) -> xr.DataArray:
"""
Compute TOA reflectance from TOA radiance
See
`here <https://www.intelligence-airbusds.com/automne/api/docs/v1.0/document/download/ZG9jdXRoZXF1ZS1kb2N1bWVudC01NTY0Mw==/ZG9jdXRoZXF1ZS1maWxlLTU1NjQy/airbus-pleiades-imagery-user-guide-15042021.pdf>`_
for more information. (Appendix D)
Args:
rad_arr (xr.DataArray): TOA Radiance array
band (BandNames): Band
Returns:
xr.DataArray: TOA Reflectance array
"""
if self.constellation == Constellation.PNEO:
band_mtd_str = _PNEO_BAND_MTD[band]
else:
band_mtd_str = _DIMAP_BAND_MTD[band]
# Get MTD XML file
root, _ = self.read_mtd()
# Get the solar irradiance value of raw radiometric Band (in watt/m2/micron)
try:
e0 = None
for br in root.iterfind(".//Band_Solar_Irradiance"):
if br.findtext("BAND_ID") == band_mtd_str:
e0 = float(br.findtext("VALUE"))
break
if e0 is None:
raise TypeError
except TypeError:
raise InvalidProductError(
"VALUE from Band_Solar_Irradiance not found in metadata!"
)
# Compute the coefficient converting TOA radiance in TOA reflectance
dt = self._sun_earth_distance_variation()
_, sun_zen = self.get_mean_sun_angles()
rad_sun_zen = np.deg2rad(sun_zen)
# WARNING: d = 1 / sqrt(d(t))
toa_refl_coeff = np.pi / (e0 * dt * np.cos(rad_sun_zen))
# LOGGER.debug(f"rad to refl coeff = {toa_refl_coeff}")
return rad_arr.copy(data=toa_refl_coeff * rad_arr)
[docs] @cache
def get_cloud_cover(self) -> float:
"""
Get cloud cover as given in the metadata
.. code-block:: python
>>> from eoreader.reader import Reader
>>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip"
>>> prod = Reader().open(path)
>>> prod.get_cloud_cover()
55.5
Returns:
float: Cloud cover as given in the metadata
"""
# Get MTD XML file
root, _ = self.read_mtd()
# Get the cloud cover
try:
cc = float(root.findtext(".//CLOUD_COVERAGE"))
except TypeError:
raise InvalidProductError("CLOUD_COVERAGE not found in metadata!")
return cc
[docs] def get_quicklook_path(self) -> str:
"""
Get quicklook path if existing.
Returns:
str: Quicklook path
"""
quicklook_path = None
try:
if self.is_archived:
quicklook_path = files.get_archived_rio_path(
self.path, file_regex=".*PREVIEW.*JPG"
)
else:
quicklook_path = str(next(self.path.glob("*PREVIEW*.JPG")))
except (StopIteration, FileNotFoundError):
LOGGER.warning(f"No quicklook found in {self.condensed_name}")
return quicklook_path
def _get_job_id(self) -> str:
"""
Get VHR job ID
Returns:
str: VHR product ID
"""
return self.split_name[-1]
@abstractmethod
def _map_bands(self) -> None:
"""
Map bands
"""
raise NotImplementedError
@abstractmethod
def _set_instrument(self) -> None:
"""
Set product type
"""
raise NotImplementedError
