def generate_toa_sub_images(self, working):
dtm_coarse = self._dem.ALC
# For each band of the input product
tmp_sub_toa_pipe = OtbPipelineManager()
for i, toa in enumerate(self._toa_scalar_list):
# undersampling at L2CoarseResolution
toa_sub_image = os.path.join(working, "aot_sub_{}.tif".format(i))
app = resample(toa,
dtm_coarse,
toa_sub_image,
OtbResampleType.LINEAR_WITH_RADIUS,
write_output=False)
self._toa_sub_list.append(app.getoutput()["out"])
tmp_sub_toa_pipe.add_otb_app(app)
# end band loop
# *******************************************************************************************************
# TOA Sub image pipeline connection
# *******************************************************************************************************
toa_sub_image = os.path.join(working, "aot_sub.tif")
param_concatenate = {"il": self._toa_sub_list, "out": toa_sub_image}
OtbAppHandler("ConcatenateDoubleImages", param_concatenate)
self._sub_toa = toa_sub_image
tmp_sub_toa_pipe.free_otb_app()
def test_otb_chain_memory(self):
"""
Test the initialization of the object
"""
l_pipeline = OtbPipelineManager()
out_memory_1 = band_math([QB_1_ORTHO_EXTRACT.get("image_fullpath", None)], "im1b1",
self.working_test_directory, write_output=False)
l_pipeline.add_otb_app(out_memory_1)
self.assertEqual(len(l_pipeline.otb_app_coarse_resolution), 1)
out_memory_2 = band_math([out_memory_1.getoutput().get("out")], "im1b1",
self.working_test_directory, write_output=False)
l_pipeline.add_otb_app(out_memory_2)
self.assertEqual(len(l_pipeline.otb_app_coarse_resolution), 2)
out_memory_3 = band_math([out_memory_2.getoutput().get("out")], "im1b1",
self.working_test_directory, write_output=True)
l_pipeline.free_otb_app()
self.assertEqual(len(l_pipeline.otb_app_coarse_resolution), 0)
def assign_zenith_and_azimuth_image_to_grid_to_image(input_image_zen, input_value_azi, output_filename):
"""
:param output_filename:
:param input_image_zen:
:param input_value_azi:
:return:
"""
band_math_1 = OtbAppHandler("BandMath", {"il": [input_image_zen, input_value_azi],
"exp": "tan(im1b1)*sin(im2b1)"}, write_output=False)
pipeline = OtbPipelineManager()
pipeline.add_otb_app(band_math_1)
band_math_2 = OtbAppHandler("BandMath", {"il": [input_image_zen, input_value_azi],
"exp": "tan(im1b1)*cos(im2b1)"}, write_output=False)
pipeline.add_otb_app(band_math_2)
concat = OtbAppHandler("ConcatenateImages", {"il": [band_math_1.getoutput().get(
"out"), band_math_2.getoutput().get("out")], "out": output_filename}, write_output=True)
pipeline.add_otb_app(concat)
return pipeline
class Sentinel2L1ImageFileReader(Sentinel2L1ImageFileReaderBase):
def __init__(self):
super(Sentinel2L1ImageFileReader, self).__init__()
self._l2edg_pipeline = OtbPipelineManager()
self._Satellite = "SENTINEL2"
self._plugin = MajaSentinel2Plugin()
def can_read(self, plugin_name):
return plugin_name == "SENTINEL2"
def generate_toa(self, listOfTOAImageFileNames,
reflectanceMultiplicationValues, working):
"""
:param listOfTOAImageFileNames: ListOfStrings
:param reflectanceMultiplicationValues: ListOfDoubles
:return:
:rtype: string
"""
# Initialization
l_ProjectionRef = ""
# =======> GENERATE TOA CACHING
l_ProjectionRef = self.generate_toa_caching(
listOfTOAImageFileNames, reflectanceMultiplicationValues, working)
LOGGER.debug("Caching TOA images done ...")
return l_ProjectionRef
def generate_edg_images_from_toa(self, listOfTOAImageFileNames, working):
"""
:param listOfTOAImageFileNames: list
:param working: path
:return:
"""
# Get the number of band with the number of TOA image files set in the input product directory
l_NbBand = len(listOfTOAImageFileNames) # int
# *******************************************************************************************************
# L2SubEDG pipeline connection
# *******************************************************************************************************
LOGGER.debug("Start IPEDGSub")
m_OneBandFilterList = []
m_ResamplingList = []
m_OrFilterList = []
tmp_edg_pipe = OtbPipelineManager()
for i in range(2):
toaFilename = listOfTOAImageFileNames[i]
LOGGER.debug(("toaFilename : ", toaFilename))
out_oneBandValue = os.path.join(working,
"OneBandValue" + str(i) + ".tif")
band_math_OneBand = one_band_equal_value(
self._toa_scalar_list[i],
output_image=out_oneBandValue + ":uint8",
threshold=0.0,
write_output=False)
tmp_edg_pipe.add_otb_app(band_math_OneBand)
m_OneBandFilterList.append(band_math_OneBand.getoutput()["out"])
out_resample = os.path.join(working,
"mask_resample" + str(i) + ".tif")
resample_mask = resample(band_math_OneBand.getoutput()["out"],
self._dem.ALC,
out_resample + ":uint8",
OtbResampleType.LINEAR_WITH_RADIUS,
threshold=0.5,
write_output=False)
tmp_edg_pipe.add_otb_app(resample_mask)
m_ResamplingList.append(resample_mask.getoutput()["out"])
for i in range(l_NbBand - 2):
out_oneBandValue = os.path.join(
working, "OneBandValue" + str(i + 2) + ".tif")
band_math_OneBand = one_band_equal_value(
self._toa_scalar_list[i + 2],
output_image=out_oneBandValue + ":uint8",
threshold=0.0,
write_output=False)
tmp_edg_pipe.add_otb_app(band_math_OneBand)
m_OneBandFilterList.append(band_math_OneBand.getoutput()["out"])
out_resample = os.path.join(working,
"mask_resample" + str(i + 2) + ".tif")
resample_mask = resample(band_math_OneBand.getoutput()["out"],
self._dem.ALC,
out_resample + ":uint8",
OtbResampleType.LINEAR_WITH_RADIUS,
threshold=0.5,
write_output=False)
tmp_edg_pipe.add_otb_app(resample_mask)
m_ResamplingList.append(resample_mask.getoutput()["out"])
out_concat = os.path.join(working, "ConcatSubEdgOneBand.tif")
param_oneband_concat = {
"il": m_ResamplingList,
"out": out_concat + ":uint8"
}
qoth_concat_app = OtbAppHandler("ConcatenateMaskImages",
param_oneband_concat,
write_output=False)
tmp_edg_pipe.add_otb_app(qoth_concat_app)
out_or0 = os.path.join(working, "MaskOrMask_0.tif")
band_math_or_b1 = one_band_equal_value(
qoth_concat_app.getoutput().get("out"),
output_image=out_or0 + ":uint8",
threshold=1)
self._edgsubmask = band_math_or_b1.getoutput().get("out")
tmp_edg_pipe.add_otb_app(band_math_or_b1)
tmp_edg_pipe.free_otb_app()
LOGGER.debug("End IPEDGSub.")
# *******************************************************************************************************
# L2EDG pipeline connection
# *******************************************************************************************************
LOGGER.debug("Start L2EDG ...")
l_BandsDefinitions = self._plugin.BandsDefinitions
LOGGER.debug("BANDS DEFINITION")
LOGGER.debug(l_BandsDefinitions)
l_ListOfL2Resolution = l_BandsDefinitions.ListOfL2Resolution # ListOfStrings
# At L2 resolution
l_NbL2Res = len(l_ListOfL2Resolution)
for r in range(l_NbL2Res):
res_str = l_ListOfL2Resolution[r]
# Set the threshold to 0.0.001 so that all pixel above 1/1000 to edge pixels
# to identify the pixel contaminated by an edge pixel after resampling
# ExpandFilterPointer => PadAndResampleImageFilter => app ressampling
out_ressampling = os.path.join(
working, "IPEDGRealL2_{}.tif".format(res_str))
l2edg_resamp_app = resample(
self._edgsubmask,
self._dem.ALTList[r],
out_ressampling + ":uint8",
OtbResampleType.LINEAR,
threshold=0.0,
write_output=(False
or is_croco_on("sentinel2.l1reader.l2edg")))
self._l2edg_pipeline.add_otb_app(l2edg_resamp_app)
self._l2edgmasklist.append(l2edg_resamp_app.getoutput().get("out"))
LOGGER.debug("End L2EDG ...")
# Can read method
def read(self, product_info, app_handler, l2comm, dem, pReadL1Mode):
"""product_info,plugin, l2comm,mode
:param product_info: L1ImageInformationsBase
:param pReadL1Mode: ReadL1ModeType
:return:
"""
product_filename = product_info.HeaderFilename
working_dir = app_handler.get_directory_manager(
).get_temporary_directory("L1Read_", do_always_remove=True)
self._plugin.initialize(app_handler)
self._GIPPL2COMMHandler = l2comm
LOGGER.info("Start Sentinel2 L1 ImageFileReader ...")
self._ReadL1Mode = pReadL1Mode
self._dem = dem
# --------------------------------------
# Check the extension of th efile. For Muscate, must be .XML (in upper case)
IsValidSatellite = (product_info.HeaderHandler is not None)
if not IsValidSatellite:
raise MajaPluginSentinel2Exception(
"The file <{}> is not a valid Sentinel2 L1 product.".format(
product_filename))
# Check the validity with the schema
lSchemaLocationFile = os.path.join(
app_handler.get_schemas_root_install_dir(),
self._plugin.MAJA_INSTALL_SCHEMAS_L1PRODUCT_FILE)
if app_handler.get_validate_schemas():
# remplacer par un appel à pylint ? un check xml
result = check_xml(product_filename, xsd_file=lSchemaLocationFile)
if not result:
raise MajaPluginSentinel2Exception(
"The xml file <{}> is not conform with its schema <{}> !, Log of execution :{}"
.format(product_filename, lSchemaLocationFile,
str(result)))
# --------------------------------------
# Load the header tile file in the xml tile handler to read tile information
xmlTileFilename = product_info.xmlTileFilename
# *******************************************************************************************************
# TOA & masks Reader connection
# *******************************************************************************************************
l_BandsDefinitions = self._plugin.BandsDefinitions # BandsDefinitions
l_ListOfTOAImageFileNames = product_info.HeaderHandler.get_list_of_toa_images(
) # ListOfStrings
l_SatPixFileNames = product_info.TileHandler.ListOfSaturatedPixelsHeaderFileName # ListOfStrings
l_DefectivPixFileNames = product_info.TileHandler.ListOfDefectivPixelsHeaderFileName # ListOfStrings
l_ZoneMaskFileNames = product_info.TileHandler.ListOfDetFootPrintHeaderFileName # ListOfStrings
l_NoDataMaskFileNames = product_info.TileHandler.ListOfNoDataPixelsHeaderFileName # ListOfStrings
l_ListOfTOABandCode = product_info.HeaderHandler.get_list_of_band_codes(
) # ListOfStrings
l_ListOfL1Resolution = l_BandsDefinitions.ListOfL1Resolution # ListOfStrings
l_ListOfL2Resolution = l_BandsDefinitions.ListOfL2Resolution # ListOfStrings
l_NbL1Res = len(l_ListOfL1Resolution) # int
l_NbL2Res = len(l_ListOfL2Resolution) # int
l_NbBand = len(l_ListOfTOAImageFileNames) # int
# TODO: check ConfigUserCameraXMLFileHandler
# TODO; check GetL2CoarseResolution grep L2CoarseResolution
l_Areas = dem.L2Areas
l_L2CoarseArea = dem.CoarseArea
l_L2CoarseResolution = int(round(dem.CoarseArea.spacing[0]))
LOGGER.debug(l_L2CoarseResolution)
l_L1NoData = product_info.L1NoData
l_ReflectanceQuantificationValue = product_info.ReflectanceQuantification
l_RealL1NoData = l_L1NoData * l_ReflectanceQuantificationValue # RealNoDataType
if (len(l_ListOfTOABandCode) != len(l_ListOfTOAImageFileNames)) or (
len(l_ListOfTOABandCode) != len(l_SatPixFileNames)) or (
len(l_ListOfTOABandCode) != len(l_DefectivPixFileNames)):
raise MajaPluginSentinel2Exception(
"Sentinel2L1ImageFileReader.CanRead: Internal error: The number of image file, sat file and band code are different !"
)
# =======> GENERATE TOA CACHING
#
# * Get calibration coeffs if activated
l_reflectanceMultiplicationValues = [] # ListOfDoubles
if xml_tools.as_bool(l2comm.get_value("CalAdjustOption")):
l_factors = xml_tools.as_float_list(
l2comm.get_value("CalAdjustFactor"))
if len(l_factors) != len(l_ListOfTOABandCode):
raise MajaPluginSentinel2Exception(
"Not the same number of Calibration coeffs than L1 bands ( {}, {} )"
.format(len(l_factors), len(l_ListOfTOABandCode)))
for i in range(len(l_ListOfTOABandCode)):
l_reflectanceMultiplicationValues.append(
l_ReflectanceQuantificationValue * l_factors[i])
else:
for i in range(len(l_ListOfTOABandCode)):
l_reflectanceMultiplicationValues.append(
l_ReflectanceQuantificationValue)
l_ProjectionRef = self._dem.ProjRef
self.generate_toa(l_ListOfTOAImageFileNames,
l_reflectanceMultiplicationValues,
working_dir) # string
# ***********************************************************************************************************
# Generate EDG at L2coarse and L2 resolutions
# ***********************************************************************************************************
self.generate_edg_images_from_toa(l_ListOfTOAImageFileNames,
working_dir)
# ***********************************************************************************************************
# Get the bounding box of each L1 resolution
# ***********************************************************************************************************
# list of bounding box per resolution
l_L1BoundingBoxMap = {} # BoundingBoxMapType
for res in range(l_NbL1Res):
curRes = l_ListOfL1Resolution[res] # string
l_IntResolution = l_BandsDefinitions.get_l1_resolution(curRes)
# Read the bounding box for the current resolution
l_BoundingBox = product_info.TileHandler.get_geoposition_boundingbox(
l_IntResolution) # BoundingBoxType
LOGGER.debug(
"S2 Geoposition BoundingBox computed: xmin, ymin, xmax, ymax: %s, %s, %s, %s.",
l_BoundingBox.xmin, l_BoundingBox.ymin, l_BoundingBox.xmax,
l_BoundingBox.ymax)
# Store the bounding box per resolution
l_L1BoundingBoxMap[curRes] = l_BoundingBox
# ***********************************************************************************************************
# ***********************************************************************************************************
# ***********************************************************************************************************
# START READ L1 for ALGORITHMS
# ***********************************************************************************************************
# ***********************************************************************************************************
# ***********************************************************************************************************
if pReadL1Mode == ReadL1Mode.READ_L1_MODE_FOR_ALGORITHMS:
# --------------------------------------
# Get information of areas (footprint) of the product (Origin, Spacing and Size for L2 and L2Coarse resolution)
# --------------------------------------
l_L2Dems = dem.ALTList
l_CoarseDem = dem.ALC
# ***********************************************************************************************************
# Get the bounding box of each L1 resolution
# ***********************************************************************************************************
# list of bounding box per resolution
l_L2CoarseBoundingBoxMap = {} # BoundingBoxMapType
for res in range(l_NbL1Res):
curRes = l_ListOfL1Resolution[res] # string
l_IntResolution = l_BandsDefinitions.get_l1_resolution(curRes)
l_PointXY = product_info.TileHandler.get_geoposition_upperleftcorner(
l_IntResolution) # PointXYType
l_L2CoarseBoundingBox = BoundingBox()
# ATTENTION: For coordinates, cf. FD 73233
l_L2CoarseBoundingBox.xmin = l_PointXY.x
l_L2CoarseBoundingBox.ymin = l_PointXY.y - l_L2CoarseArea.size[
1] * l_L2CoarseResolution
l_L2CoarseBoundingBox.xmax = l_PointXY.x + l_L2CoarseArea.size[
0] * l_L2CoarseResolution
l_L2CoarseBoundingBox.ymax = l_PointXY.y
l_L2CoarseBoundingBoxMap[curRes] = l_L2CoarseBoundingBox
LOGGER.debug("Start SubSampling ...")
# =======> GENERATE TOA SUB IMAGES AT L2 COARSE RESOLUTION
self.generate_toa_sub_images(working_dir)
LOGGER.debug("Start SubSampling done.")
# *******************************************************************************************************
# L2 TOA image pipeline connection
# ******************* ************************************************************************************
# =======> GENERATE L2 TOA IMAGES
self.generate_l2_toa_images(working_dir)
# *******************************************************************************************************
# Rasterize the gml mask per L2 resolution per band
# *******************************************************************************************************
# =======> GENERATE MASK RASTERS
self.generate_mask_rasters_gml(
l_L1BoundingBoxMap, l_Areas, l_ProjectionRef,
l_SatPixFileNames, l_DefectivPixFileNames, l_ZoneMaskFileNames,
self._plugin.
GDAL_RASTERIZE_GML_MASK_ADDITIONAL_COMMAND_LINE_PARAMETERS,
self._plugin.
GDAL_RASTERIZE_GML_DETECTOR_ADDITIONAL_COMMAND_LINE_PARAMETERS,
working_dir)
l_CurrentConfigUserCamera = self._plugin.ConfigUserCamera
grid_ref_alt = l_CurrentConfigUserCamera.get_Algorithms(
).get_GRID_Reference_Altitudes()
l_VIEHRef = grid_ref_alt.get_VIEHRef() # double
l_SOLH1 = grid_ref_alt.get_SOLH1() # double
# *******************************************************************************************************
# IPSOL Sub image pipeline connection
# *******************************************************************************************************
LOGGER.debug("Start IPSOL SubSampling ...")
l_solarAngleFile = product_info.TileHandler.angles.sun_angles.write(
working_dir)
# =======> GENERATE SOLAR ANGLE GRIDS
self.get_solar_grids(self._dem.ALC, l_solarAngleFile, l_SOLH1,
working_dir)
LOGGER.debug("Start IPSOL SubSampling done.")
# *******************************************************************************************************
# IPVIE Sub image pipeline connection
# *******************************************************************************************************
LOGGER.debug("Start IPVIE SubSampling ...")
LOGGER.debug("For each band ...")
for l_band in range(l_NbBand):
l_L1Resolution = l_BandsDefinitions.get_l1_resolution_for_band_code(
l_ListOfTOABandCode[l_band]) # string
# Get the list of detectors (index)
l_ListOfZone = product_info.TileHandler.get_list_of_zones(
l_band) # ListOfUIntegers
l_BoundingBox = l_L2CoarseBoundingBoxMap[
l_L1Resolution] # BoundingBoxType
# Get the list of viewing angles in the header file of the tile
l_vieAngles = product_info.TileHandler.angles.viewing_incidence_angle.get_viewing_angles(
l_band) # ListOfListOfStrings
if len(l_vieAngles) == 0:
raise MajaPluginSentinel2Exception(
"No viewing angles for band " + str(l_band))
# =======> GENERATE VIEWING ANGLE GRIDS
self.get_viewing_grids(
l_band, l_L2CoarseArea, l_ProjectionRef, l_VIEHRef,
l_SatPixFileNames[l_band], l_ZoneMaskFileNames[l_band],
l_NoDataMaskFileNames[l_band], l_ListOfZone, l_BoundingBox,
l_vieAngles, self._plugin.
GDAL_RASTERIZE_GML_MASK_ADDITIONAL_COMMAND_LINE_PARAMETERS,
self._plugin.
GDAL_RASTERIZE_GML_DETECTOR_ADDITIONAL_COMMAND_LINE_PARAMETERS,
self._plugin.
GDAL_RASTERIZE_GML_NODATA_ADDITIONAL_COMMAND_LINE_PARAMETERS,
l_NbBand, working_dir)
LOGGER.debug("Start IPVIE SubSampling done.")
# To check mean angle values
# TODO: TBD
# zones = self.m_ViewingZenithMeanMap.keys()
# values = zip(zones,
# [self.m_ViewingZenithMeanMap.get(zone) for zone in zones],
# [self.m_ViewingAzimuthMeanMap.get(zone) for zone in zones])
# for zone, zenith, azimuth in values:
# LOGGER.debug(" Mean for zone %s is [zen;azi] : [%s;%s].", zone, zenith, azimuth)
# *******************************************************************************************************
# IPEDGSub and L2EDG pipeline connection
# *******************************************************************************************************
# *******************************************************************************************************
# IPSAT Sub and L2SAT image pipeline connection
# *******************************************************************************************************
# =======> GENERATE SAT IMAGES
self.generate_sat_images(working_dir)
# *******************************************************************************************************
# CLA image pipeline connection
# *******************************************************************************************************
# =======> GENERATE CLA IMAGES
self.generate_cla_images(l_RealL1NoData, working_dir)
# Fill the datas
self.dict_of_vals["IPEDGSubOutput"] = self._edgsubmask
self.dict_of_vals["SOL1Image"] = self._sol1image
l_DTMBandCode = xml_tools.as_string_list(
l2comm.get_value("DTMViewingDirectionBandCode"))[0]
l_DTMBandIdx = self._plugin.BandsDefinitions.get_band_id_in_l2_coarse(
l_DTMBandCode)
LOGGER.info("DTMBandCode= " + l_DTMBandCode)
self.dict_of_vals["DTMVIEImage"] = self._vieimagelist[l_DTMBandIdx]
self.dict_of_vals["IPTOASubOutput"] = self._sub_toa
self.dict_of_vals["L2TOAImageList"] = self._l2toaimagelist
self.dict_of_vals["ViewingZenithMeanMap"] = self._meanZenithMap
self.dict_of_vals["ViewingAzimuthMeanMap"] = self._meanAzimuthMap
self.dict_of_vals["CLAImage"] = self._claimage
self.dict_of_vals["IPSATSubOutput"] = self._subsatimage
l_CLDBandCode = l2comm.get_value("CLDViewingDirectionBandCode")
l_CLDBandIdx = self._plugin.BandsDefinitions.get_band_id_in_l2_coarse(
l_CLDBandCode)
self.dict_of_vals["ShadowVIEImage"] = self._vieimagelist[
l_CLDBandIdx]
l_CirrusBandCode = l2comm.get_value("CirrusBandCode")
l_CirrusBandIdx = self._plugin.BandsDefinitions.get_band_id_in_l2_coarse(
l_CirrusBandCode)
self.dict_of_vals["L1TOACirrusImage"] = self._toa_scalar_list[
l_CirrusBandIdx]
self.dict_of_vals["AngleZoneMaskList"] = self._l2zoneimagelist
self.dict_of_vals["L2EDGOutputList"] = self._l2edgmasklist
self.dict_of_vals["L2SATImageList"] = self._l2satmasklist
self.dict_of_vals["L2PIXImageList"] = self._l2piximagelist
class ModuleAOTEstimationComputation(MajaModule):
NAME = "AOTEstimation"
def __init__(self):
super(ModuleAOTEstimationComputation, self).__init__()
self._aot_pipeline = OtbPipelineManager()
self.in_keys_to_check = [
"Params.Caching", "Params.WriteL2ProductToL2Resolution",
"Params.DarkBandIndex_DateD", "Params.DarkBandIndex_DateD",
"Params.AOTMethod", "Params.InitMode", "Params.MTAOTbandsDateD",
"AppHandler", "Plugin", "L1Reader", "L2COMM", "DEM", "L2TOCR",
"L2SITE"
]
self.out_keys_to_check = ["AtmoAbsIPTOAC", "dtm_shd", "cr_lut"]
self.out_keys_provided = ["AOT_Sub"]
def cleanup(self):
self._aot_pipeline.free_otb_app()
def run(self, dict_of_input, dict_of_output):
LOGGER.info("AOT estimation computation start")
LOGGER.debug("Caching %s", dict_of_input.get("Params").get("Caching"))
l_caching = dict_of_input.get("Params").get("Caching")
l_writeL2 = dict_of_input.get("Params").get(
"WriteL2ProductToL2Resolution")
aot_working = dict_of_input.get(
"AppHandler").get_directory_manager().get_temporary_directory(
"AOTEstimationProc_", do_always_remove=True)
init_Mode = dict_of_input.get("Params").get("InitMode")
bands_definition = dict_of_input.get("Plugin").BandsDefinitions
# TODO concatenate cloud masks
param_darkest = {
"toac":
dict_of_output["AtmoAbsIPTOAC"],
"cld":
dict_of_output[constants.CLOUD_MASK_ALL],
"dtm":
dict_of_input.get("DEM").ALC,
"edg":
dict_of_input.get("L1Reader").get_value("IPEDGSubOutput"),
"shd":
dict_of_output["dtm_shd"],
"lutheader":
dict_of_input.get("L2TOCR"),
"lutimage":
dict_of_output["cr_lut"],
"darkestchanneltoac":
dict_of_input.get("Params").get("DarkBandIndex_DateD"),
"thresholddarkpixel":
float(dict_of_input.get("L2SITE").get_value("DarkPixelThreshold")),
"darksurfacereflectance":
float(
dict_of_input.get("L2SITE").get_value(
"DarkestPixelSurfaceReflectance")),
"darkestchannelrtc":
dict_of_input.get("Params").get("DarkBandIndex_DateD")
}
if not init_Mode:
param_darkest["l2ndt"] = dict_of_input.get("L2Reader").get_value(
"NDTImage")
param_darkest["l2rtc"] = dict_of_input.get("L2Reader").get_value(
"RTCImage")
else:
param_darkest["init"] = init_Mode
darkest_app = OtbAppHandler("DarkestPixelAOT",
param_darkest,
write_output=True)
aot_computed = darkest_app.getoutput()["aotcomputed"]
darkest_aot = darkest_app.getoutput()["darkestaot"]
l_aotmethod = dict_of_input.get("Params").get("AOTMethod")
LOGGER.debug("AOT Estimation Computation Method: " + str(l_aotmethod))
#AOT Params xml holder
xml_aot_param = AOT_Estimation()
#Common parameters
xml_aot_param.set_Sampling_Interval_Min(
dict_of_input.get("L2COMM").get_value_i("SamplingIntervalMin"))
xml_aot_param.set_Sampling_Interval_Max(
dict_of_input.get("L2COMM").get_value_i("SamplingIntervalMax"))
xml_aot_param.set_Increment_N_Distance(
dict_of_input.get("L2COMM").get_value_i("IncrementNDistance"))
xml_aot_param.set_Half_Window_Size(
dict_of_input.get("L2COMM").get_value_i("HalfWindowSize"))
xml_aot_param.set_Max_Iterations(
dict_of_input.get("L2COMM").get_value_i("MaxIterations"))
xml_aot_param.set_Step_AOT(
dict_of_input.get("L2COMM").get_value_i("StepAOT"))
xml_aot_param.set_N_Pix_Min(
dict_of_input.get("L2COMM").get_value_i("NPixMin"))
xml_aot_param.set_F_Tolerance(
dict_of_input.get("L2COMM").get_value_f("FTolerance"))
xml_aot_param.set_G_Tolerance(
dict_of_input.get("L2COMM").get_value_f("GTolerance"))
xml_aot_param.set_X_Tolerance(
dict_of_input.get("L2COMM").get_value_f("XTolerance"))
xml_aot_param.set_Epsilon_Function(
dict_of_input.get("L2COMM").get_value_f("EpsilonFunction"))
xml_aot_param.set_AOT_initial_D(
dict_of_input.get("L2COMM").get_value_f("AOTinitialD"))
xml_aot_param.set_W_dark(
dict_of_input.get("L2COMM").get_value_f("Wdark"))
xml_aot_param.set_W_bounds(
dict_of_input.get("L2COMM").get_value_f("Wbounds"))
xml_aot_param.set_AOT_min(
dict_of_input.get("L2COMM").get_value_f("AOTmin"))
xml_aot_param.set_AOT_Max_Age(
dict_of_input.get("L2COMM").get_value_f("AOTMaxAge"))
xml_aot_param.set_SE_Mask_Dilatation_Radius(
dict_of_input.get("L2COMM").get_value_f("SEMaskDilatationRadius"))
xml_aot_param.set_SE_Valid_AOT_Radius(
dict_of_input.get("L2COMM").get_value_f("SEValidAOTRadius"))
aot_image = os.path.join(aot_working, "aot_computed.tif")
aot_mask_estimation = os.path.join(aot_working,
"aot_mask_computed.tif")
param_aot = {
"toac": dict_of_output["AtmoAbsIPTOAC"],
"sat": dict_of_input.get("L1Reader").get_value("IPSATSubOutput"),
"was": dict_of_output["WaterMask"],
"dtm": dict_of_input.get("DEM").ALC,
"edg": dict_of_input.get("L1Reader").get_value("IPEDGSubOutput"),
"shd": dict_of_output["dtm_shd"],
"lutheader": dict_of_input.get("L2TOCR"),
"lutimage": dict_of_output["cr_lut"],
"cld": dict_of_output[constants.CLOUD_MASK_ALL],
"reall2nodata": dict_of_input.get("Params").get("RealL2NoData"),
"jday": dict_of_input.get("Params").get("JDay"),
"darkestaot": darkest_aot,
"aotcomputed": aot_image,
"aotmask": aot_mask_estimation
}
if init_Mode:
param_aot["init"] = init_Mode
if l_aotmethod == AOTEstimation.MULTISPECTRAL:
param_aot["mode"] = "multispectral"
elif l_aotmethod == AOTEstimation.MULTITEMPORAL:
param_aot["mode"] = "multitemporal"
elif l_aotmethod == AOTEstimation.SPECTROTEMPORAL:
param_aot["mode"] = "spectrotemporal"
if l_aotmethod == AOTEstimation.SPECTROTEMPORAL or l_aotmethod == AOTEstimation.MULTITEMPORAL:
stdevbandid = bands_definition.get_band_id_in_l2_coarse(
dict_of_input.get("L2COMM").get_value("StdevBandCode"))
if not init_Mode:
param_aot["temporal.lutmap"] = dict_of_input.get(
"L2Reader").get_value("LTCImage")
param_aot["temporal.altitudemean"] = dict_of_input.get(
"DEM").ALT_Mean
xml_aot_param.set_Multi_Temporal_Method(
Multi_Temporal_MethodType())
xml_aot_param.get_Multi_Temporal_Method().set_First_AOT(
dict_of_input.get("L2COMM").get_value_f("FirstAOT"))
xml_aot_param.get_Multi_Temporal_Method().set_Second_AOT(
dict_of_input.get("L2COMM").get_value_f("SecondAOT"))
xml_aot_param.get_Multi_Temporal_Method().set_TOA_Reflectance_Min(
dict_of_input.get("L2COMM").get_value_f("TOAReflectanceMin"))
xml_aot_param.get_Multi_Temporal_Method().set_TOA_Reflectance_Max(
dict_of_input.get("L2COMM").get_value_f("TOAReflectanceMax"))
xml_aot_param.get_Multi_Temporal_Method().set_TOA_Reflectance_Step(
dict_of_input.get("L2COMM").get_value_f("TOAReflectanceStep"))
xml_aot_param.get_Multi_Temporal_Method(
).set_Min_Difference_Thresholds_Calculation(
dict_of_input.get("L2COMM").get_value_f(
"MinDifferenceThreshold"))
xml_aot_param.get_Multi_Temporal_Method().set_Relative_Var_Max(
dict_of_input.get("L2COMM").get_value_f("RelativeVarMax"))
xml_aot_param.get_Multi_Temporal_Method().set_Var_Band_Code_D(
dict_of_input.get("Params").get("VarBandIndex_DateD"))
xml_aot_param.get_Multi_Temporal_Method().set_Var_Band_Code_Dm1(
dict_of_input.get("Params").get("VarBandIndex_DateDm1"))
xml_aot_param.get_Multi_Temporal_Method().set_Stdev_Band_Code(
stdevbandid)
xml_aot_param.get_Multi_Temporal_Method().set_Std_Noise(
dict_of_input.get("L2COMM").get_value_f("StdNoise"))
xml_aot_param.get_Multi_Temporal_Method().set_Stdev_Min(
dict_of_input.get("L2COMM").get_value_f("StdevMin"))
xml_aot_param.get_Multi_Temporal_Method().set_AOT_initial_Dm1(
dict_of_input.get("L2COMM").get_value_f("AOTinitialDm1"))
xml_aot_param.get_Multi_Temporal_Method().set_W_MT_equation2(
dict_of_input.get("L2COMM").get_value_f("WMTequation2"))
xml_aot_param.get_Multi_Temporal_Method().set_MT_AOT_Band_Code_D([
str(i)
for i in dict_of_input.get("Params").get("MTAOTbandsDateD")
])
xml_aot_param.get_Multi_Temporal_Method(
).set_MT_AOT_Band_Reflectance_Composite_Dm1([
str(i) for i in dict_of_input.get("Params").get(
"MTAOTbandsOfTheReflectancesComposite_DateDm1")
])
xml_aot_param.get_Multi_Temporal_Method(
).set_MT_AOT_Band_LUT_Composite_Dm1([
str(i) for i in dict_of_input.get("Params").get(
"MTAOTbandsOfTheLTCComposite_DateDm1")
])
if l_aotmethod == AOTEstimation.MULTISPECTRAL or l_aotmethod == AOTEstimation.SPECTROTEMPORAL:
lMSAOTRedBand = bands_definition.get_band_id_in_l2_coarse(
dict_of_input.get("L2COMM").get_value("MSAOTRedBandCode"))
lNDVIAOTBand1 = bands_definition.get_band_id_in_l2_coarse(
dict_of_input.get("L2COMM").get_value("NDVIAOTBand1Code"))
lNDVIAOTBand2 = bands_definition.get_band_id_in_l2_coarse(
dict_of_input.get("L2COMM").get_value("NDVIAOTBand2Code"))
l_AOTUsedBandCodes = xml_tools.as_string_list(
dict_of_input.get("L2COMM").get_value("MSAOTBandCode"))
lMSAOTbands = [
str(bands_definition.get_band_id_in_l2_coarse(f))
for f in l_AOTUsedBandCodes
]
xml_aot_param.set_Multi_Spectral_Method(
Multi_Spectral_MethodType())
xml_aot_param.get_Multi_Spectral_Method().set_MS_AOT_Band_Code(
lMSAOTbands)
xml_aot_param.get_Multi_Spectral_Method().set_MS_AOT_Red_Band_Code(
lMSAOTRedBand)
xml_aot_param.get_Multi_Spectral_Method().set_NDVI_AOT_Band1_Code(
lNDVIAOTBand1)
xml_aot_param.get_Multi_Spectral_Method().set_NDVI_AOT_Band2_Code(
lNDVIAOTBand2)
xml_aot_param.get_Multi_Spectral_Method().set_Red_TOA_Threshold(
dict_of_input.get("L2COMM").get_value_f("RedTOAThreshold"))
xml_aot_param.get_Multi_Spectral_Method().set_NDVI_Threshold(
dict_of_input.get("L2COMM").get_value_f("NDVIThreshold"))
xml_aot_param.get_Multi_Spectral_Method().set_MS_Slope(
dict_of_input.get("L2COMM").get_value_f("MSSlope"))
xml_aot_param.get_Multi_Spectral_Method().set_MS_YIntercept(
dict_of_input.get("L2COMM").get_value_f("MSYIntercept"))
xml_aot_param.get_Multi_Spectral_Method().set_NDVI_Slope(
dict_of_input.get("L2COMM").get_value_f("NDVISlope"))
xml_aot_param.get_Multi_Spectral_Method().set_NDVI_YIntercept(
dict_of_input.get("L2COMM").get_value_f("NDVIYIntercept"))
xml_aot_param.get_Multi_Spectral_Method().set_W_MS(
dict_of_input.get("L2COMM").get_value_f("WMS"))
if l_aotmethod == AOTEstimation.SPECTROTEMPORAL:
spectrotemp = Spectro_Temporal_MethodType()
xml_aot_param.set_Spectro_Temporal_Method(spectrotemp)
xml_aot_param.get_Spectro_Temporal_Method(
).set_MT_Weight_Threshold(
dict_of_input.get("L2COMM").get_value_f("MTWeightThreshold"))
xml_aot_param.get_Spectro_Temporal_Method().set_A_Coefficient(
dict_of_input.get("L2COMM").get_value_f("ACoefficient"))
xml_aot_param.get_Spectro_Temporal_Method().set_B_Coefficient(
dict_of_input.get("L2COMM").get_value_f("BCoefficient"))
const_mask_filename = os.path.join(aot_working, "cloud_constzero.tif")
constant_app = constant_image(dict_of_input.get("DEM").ALC,
0,
const_mask_filename + ":uint8",
write_output=l_caching)
constant_mask = constant_app.getoutput()["out"]
if dict_of_input.get("Params").get("SnowBandAvailable"):
param_aot["snow"] = dict_of_output.get("cld_snow")
else:
param_aot["snow"] = constant_mask
if not init_Mode:
param_aot["l2ndt"] = dict_of_input.get("L2Reader").get_value(
"NDTImage")
param_aot["l2pxd"] = dict_of_input.get("L2Reader").get_value(
"PXDImage")
param_aot["l2rtc"] = dict_of_input.get("L2Reader").get_value(
"RTCImage")
param_aot["l2rta"] = dict_of_input.get("L2Reader").get_value(
"RTAImage")
# Cams related parameters
if dict_of_input.get("Params").get("UseCamsData"):
xml_aot_param.set_AOT_KPondCAMS(
dict_of_input.get("L2COMM").get_value_f("KPondsCams"))
xml_aot_param.set_AOT_HeightScale(
dict_of_input.get("L2COMM").get_value_f("HeighScale"))
param_aot["cams.nodatatau"] = dict_of_input.get("Params").get(
"RealL2NoData")
param_aot["cams.altmean"] = dict_of_input.get("DEM").ALT_Mean
param_aot["cams.aot"] = dict_of_input.get("CAMS_AOT")
aot_cams_image = os.path.join(aot_working, "aot_cams.tif")
param_aot["aotcams"] = aot_cams_image
#Write down the xml interface
output = io.StringIO()
output.write('<?xml version="1.0" ?>\n')
xml_aot_param.export(output,
0,
name_='AOTEstimation',
namespacedef_='',
pretty_print=True)
l_xml_param_filename = os.path.join(aot_working, "AOTParams.xml")
with open(l_xml_param_filename, "w") as fh:
fh.write(output.getvalue().replace(" ", " "))
LOGGER.info("Writed aot params to " + l_xml_param_filename)
param_aot["xmlparamsfile"] = l_xml_param_filename
# Effectively launch the app
aot_app = OtbAppHandler("AOTEstimation",
param_aot,
write_output=l_caching)
if not l_caching:
self._aot_pipeline.add_otb_app(aot_app)
aot_mean = aot_app.getoutput()["aotmean"]
aot_valid = aot_app.getoutput()["aotvalid"]
# If cams used then no gapfilling needed
if dict_of_input.get("Params").get("UseCamsData"):
smoothing_input = aot_app.getoutput()["aotcomputed"]
mask_input = aot_app.getoutput()["aotmask"]
else:
aot_gapfilled_image = os.path.join(aot_working,
"aot_gapfilled.tif")
aot_gapfilled_mask = os.path.join(aot_working,
"aot_gapfilled_mask.tif")
param_gapfilling = {
"im":
aot_app.getoutput()["aotcomputed"],
"initwindowsradius":
dict_of_input.get("L2COMM").get_value_i("InitWindowRadius"),
"maxwindowsradius":
dict_of_input.get("L2COMM").get_value_i("MaxWindowRadius"),
"reall2nodata":
dict_of_input.get("Params").get("RealL2NoData"),
"mean":
aot_mean,
"hasvalidpixels":
aot_valid,
"defaultvalue":
dict_of_input.get("L2COMM").get_value_f("DefaultAOT"),
"out":
aot_gapfilled_image,
"outmask":
aot_gapfilled_mask + ":uint8"
}
gap_filling_app = OtbAppHandler("GapFilling",
param_gapfilling,
write_output=(l_caching
or l_writeL2))
if not (l_caching or l_writeL2):
self._aot_pipeline.add_otb_app(gap_filling_app)
smoothing_input = gap_filling_app.getoutput()["out"]
mask_input = gap_filling_app.getoutput()["outmask"]
aot_smoothed_image = os.path.join(aot_working, "aot_smoothed.tif")
param_smoothing = {
"in":
smoothing_input,
"out":
aot_smoothed_image,
"type":
"gaussian",
"type.gaussian.radius":
dict_of_input.get("L2COMM").get_value_f("SigmaSmoothingAOT"),
"type.gaussian.maxkernelwidth":
4.0 * dict_of_input.get("L2COMM").get_value_f("SigmaSmoothingAOT"),
"type.gaussian.maxerror":
1e-15
}
smoothed_app = OtbAppHandler("SmoothingBis",
param_smoothing,
write_output=l_caching)
if (not l_caching):
self._aot_pipeline.add_otb_app(smoothed_app)
aot_sub_image = os.path.join(aot_working, "aot_sub.tif")
param_subresampling = {
"dtm": dict_of_input.get("DEM").ALC,
"im": smoothed_app.getoutput()["out"],
"interp": "linear",
"out": aot_sub_image
}
subresampling_app = OtbAppHandler("Resampling",
param_subresampling,
write_output=(l_caching
or l_writeL2))
if (not (l_caching or l_writeL2)):
self._aot_pipeline.add_otb_app(subresampling_app)
dict_of_output["AOT_Sub"] = subresampling_app.getoutput()["out"]
if l_writeL2:
l_nbRes = len(bands_definition.ListOfL2Resolution)
aot_list = []
aotmask_list = []
for r in range(0, l_nbRes):
l_res = bands_definition.ListOfL2Resolution[r]
aot_image = os.path.join(aot_working, "aot_" + l_res + ".tif")
aot_mask = os.path.join(aot_working,
"aotmask_" + l_res + ".tif")
param_subresampling["dtm"] = dict_of_input.get(
"DEM").ALTList[r]
param_subresampling["im"] = subresampling_app.getoutput(
)["out"]
param_subresampling["out"] = aot_image
resamp_aot_app = OtbAppHandler("Resampling",
param_subresampling)
dict_of_output["AOT_" +
l_res] = resamp_aot_app.getoutput().get("out")
param_maskresampling = {
"dtm": dict_of_input.get("DEM").ALTList[r],
"im": mask_input,
"interp": "linear",
"out": aot_mask + ":uint8",
"threshold": 0.25
}
resamp_aotmask_app = OtbAppHandler("Resampling",
param_maskresampling)
dict_of_output["AOTMASK_" +
l_res] = resamp_aotmask_app.getoutput().get(
"out")
aot_list.append(resamp_aot_app.getoutput().get("out"))
aotmask_list.append(resamp_aotmask_app.getoutput().get("out"))
dict_of_output["L2AOTList"] = aot_list
dict_of_output["L2AOTMaskList"] = aotmask_list
class DEMBase(object):
def __init__(self):
self.ALTList = []
self.ALT_Mean = 0
self.ALT_Min = 0
self.ALT_Max = 0
self.ALT_Stdv = 0
self.ALT_LogicalName = ""
self.__ASPListInternal = []
self.ASPList = []
self.__SLPListInternal = []
self.SLPList = []
self.ALC = ""
self.MSK = ""
self.ASC = None
self.__SLCInternal = ""
self.SLC = ""
self.Satellite = ""
self._coeff = 0.01
self._resList = []
self._expr = "im1b1 * "
self.CoarseArea = None
self.L2Areas = []
self.ProjRef = ""
self.ProjCode = ""
self.ProjType = ""
self.ProjName = ""
self.Site = ""
self._apps = OtbPipelineManager()
def initialize(self, filename, working_dir, has_snow):
file_hdr = os.path.splitext(filename)[0] + ".HDR"
file_dbl = os.path.splitext(filename)[0] + ".DBL"
file_dbldir = os.path.splitext(filename)[0] + ".DBL.DIR"
LOGGER.info("AUX_REFDE2 filename: " + filename)
# uncompress dbl
uncompress_dbl_product(file_dbl)
# DEM filenames provider
list_of_file = os.listdir(file_dbldir)
nbresol = 0
for f in list_of_file:
if "_ALT" in f and "TIF" in os.path.splitext(f)[1]:
nbresol = nbresol + 1
LOGGER.info("Nb resolution found " + str(nbresol))
self.initialize_res_list(nbresol)
LOGGER.info(
"DEMFilenamesProvider::Initialize. Nb resolution computed:" +
str(len(self._resList)))
for resol in self._resList:
LOGGER.debug("DEMFilenamesProvider::Initialize. Prefix resol : " +
resol)
handler = EarthExplorerXMLFileHandler(file_hdr)
list_of_dbl_files = handler.get_list_of_packaged_dbl_files(True, False)
LOGGER.info("DEMFileNames found " + str(len(list_of_dbl_files)) +
" files")
for i in range(0, len(list_of_dbl_files)):
if list_of_dbl_files[i].split('.TIF')[-1]:
raise MajaDataException(
"Wrong file extension detected. Delete the file: " +
str(list_of_dbl_files[i]))
# --------------------------------------
# Find the correct filename
for fi in list_of_dbl_files:
# LAIG - FA - MAC - 1610 - CNES
# ../ CONTEXTES_ANOMALIES / TMA_VENUS_maccs_errors / 4398 / VE_TEST_AUX_REFDE2_BRASCHAT_0001.DBL.DIR / VE_TEST_AUX_REFDE2_BRASCHAT_0001_SLP.TIF
# Extract the last value -> SLP
# ../ CONTEXTES_ANOMALIES / TMA_VENUS_maccs_errors / 4398 / VE_TEST_AUX_REFDE2_BRASCHAT_0001.DBL.DIR / VE_TEST_AUX_REFDE2_BRASCHAT_0001_ALT_R1.TIF
# Extract the last value -> ALT
l_splitted = (os.path.splitext(os.path.basename(fi))[0]).split("_")
l_lenghtlistfilenamename = len(l_splitted)
# Extract the tow last values -> ex: 0001 _SLP or ALT_R1
l_keytype = l_splitted[-1]
if l_lenghtlistfilenamename > 2:
l_keytype = l_splitted[-2] + "_" + l_keytype
# --------------------------------------
# Test if the filename is ALC
if "ALC" in l_keytype:
self.ALC = fi
# --------------------------------------
# Test if the filename is MSK
elif "MSK" in l_keytype:
self.MSK = fi
# --------------------------------------
# Test if the filename is ASC
elif "ASC" in l_keytype:
self.ASC = fi
# --------------------------------------
# Test if the filename is SLC
elif "SLC" in l_keytype:
self.__SLCInternal = fi
else:
# --------------------------------------
# Lop under resolutions
for res in self._resList:
# --------------------------------------
# Test if the filename is SLP
if "SLP" in l_keytype:
if res in l_keytype:
self.__SLPListInternal.append(fi)
# --------------------------------------
# Test if the filename is ALT
elif "ALT" in l_keytype:
if res in l_keytype:
self.ALTList.append(fi)
# --------------------------------------
# Test if the filename is ASP
elif "ASP" in l_keytype:
if res in l_keytype:
self.__ASPListInternal.append(fi)
else:
LOGGER.debug(
"Unknown Filename and associated product type.")
# endloop resol
# --------------------------------------
# Check existent of ALC filename
if not os.path.exists(self.ALC):
raise MajaDataException("The ALC file '" + self.ALC +
"' of the DTM doesn't exist !")
# --------------------------------------
# Check existent of MSK filename
if not os.path.exists(self.MSK):
raise MajaDataException("The MSK file '" + self.MSK +
"' of the DTM doesn't exist !")
# --------------------------------------
# Check existent of SLC filename
if not os.path.exists(self.__SLCInternal):
raise MajaDataException("The SLC file '" + self.__SLCInternal +
"' of the DTM doesn't exist !")
else:
LOGGER.debug("Starting multiply " + self.__SLCInternal + " * " +
str(self._coeff))
self.SLC = os.path.join(
working_dir, "Mul_" + os.path.basename(self.__SLCInternal))
self._apps.add_otb_app(
multiply_by_scalar(self.__SLCInternal,
self._coeff,
output_image=self.SLC))
mtdat = GdalDatasetInfo(self.__SLCInternal)
self.CoarseArea = Area()
self.CoarseArea.size = mtdat.size
self.CoarseArea.origin = mtdat.origin
self.CoarseArea.spacing = mtdat.pixel_size
LOGGER.debug("Done")
# --------------------------------------
for resol in range(0, len(self._resList)):
# --------------------------------------
# Check existent of SLP filename
if not os.path.exists(self.__SLPListInternal[resol]):
raise MajaDataException("One of the SLP file '" +
self.__SLPListInternal[resol] +
"' of the DTM doesn't exist !")
else:
LOGGER.debug("Starting multiply " +
self.__SLPListInternal[resol] + " * " +
str(self._coeff))
tmp = os.path.join(
working_dir,
"Mul_" + os.path.basename(self.__SLPListInternal[resol]))
slp_mul_app = multiply_by_scalar(self.__SLPListInternal[resol],
self._coeff,
output_image=tmp,
write_output=False)
self._apps.add_otb_app(slp_mul_app)
mtdat = GdalDatasetInfo(self.__SLPListInternal[resol])
l2area = Area()
l2area.size = mtdat.size
l2area.origin = mtdat.origin
l2area.spacing = mtdat.pixel_size
self.ProjRef = mtdat.dataset.GetProjectionRef()
self.L2Areas.append(l2area)
LOGGER.debug("Done")
self.SLPList.append(slp_mul_app.getoutput().get("out"))
# --------------------------------------
# Check existent of ALT filename
if not os.path.exists(self.ALTList[resol]):
raise MajaDataException("One of the ALT file '" +
self.ALTList[resol] +
"' of the DTM doesn't exist !")
# --------------------------------------
# Check existent of ASP filename
if not os.path.exists(self.__ASPListInternal[resol]):
raise MajaDataException("One of the ASP file '" +
self.__ASPListInternal[resol] +
"' of the DTM doesn't exist !")
else:
LOGGER.debug("Starting multiply " +
self.__ASPListInternal[resol] + " * " +
str(self._coeff))
tmp = os.path.join(
working_dir,
"Mul_" + os.path.basename(self.__ASPListInternal[resol]))
asp_mul_app = multiply_by_scalar(self.__ASPListInternal[resol],
self._coeff,
output_image=tmp,
write_output=False)
self._apps.add_otb_app(asp_mul_app)
LOGGER.debug("Done")
self.ASPList.append(asp_mul_app.getoutput().get("out"))
# end loop resol
LOGGER.debug(nbresol)
l_cartoCode = xml_tools.get_only_value(
handler.root,
"//DEM_Information/Cartographic/Coordinate_Reference_System/Code",
namespaces=handler.nss,
check=True)
l_geoCode = xml_tools.get_only_value(
handler.root,
"//DEM_Information/Geographic/Coordinate_Reference_System/Code",
namespaces=handler.nss,
check=True)
if l_cartoCode is not None:
self.ProjCode = l_cartoCode.text
self.ProjType = "PROJECTED"
elif l_geoCode is not None:
self.ProjCode = l_geoCode.text
self.ProjType = "GEOGRAPHIC"
else:
raise MajaDataException("Unknown DEM type")
LOGGER.debug("DEM Projection Code: " + self.ProjCode)
LOGGER.debug("DEM Projection Type: " + self.ProjType)
self.Site = xml_tools.get_xml_string_value(
handler.root,
"//Specific_Product_Header/Instance_Id/Applicable_Site_Nick_Name",
namespaces=handler.nss)
if nbresol != 0:
param_stats = {"im": self.ALTList[0]}
stat_app = stats(self.ALTList[0])
self.ALT_Mean = stat_app.getoutput().get("mean")
self.ALT_Max = stat_app.getoutput().get("max")
self.ALT_Min = stat_app.getoutput().get("min")
self.ALT_Stdv = stat_app.getoutput().get("stdv")
self.ALT_LogicalName = "LOCAL=" + os.path.splitext(
os.path.basename(file_hdr))[0]
LOGGER.info("DEM Mean : " + str(self.ALT_Mean))
LOGGER.info("DEM Max : " + str(self.ALT_Max))
LOGGER.info("DEM Min : " + str(self.ALT_Min))
LOGGER.info("DEM Stdv : " + str(self.ALT_Stdv))
def initialize_res_list(self, nb_resol):
if nb_resol <= 1:
self._resList.append("")
else:
for i in range(0, nb_resol):
self._resList.append("_R" + str(i + 1))
def __del__(self):
self._apps.free_otb_app()
class Sentinel2MuscateL1ImageFileReader(Sentinel2L1ImageFileReaderBase):
def __init__(self):
super(Sentinel2MuscateL1ImageFileReader, self).__init__()
self._Satellite = "SENTINEL2"
self._plugin = MajaSentinel2MuscatePlugin()
self._toa_pipeline = OtbPipelineManager()
self._l2_dfp_pipeline = OtbPipelineManager()
self._l2_sat_pipeline = OtbPipelineManager()
self._sub_sat_pipeline = OtbPipelineManager()
self._l2_detf_pipeline = OtbPipelineManager()
self._l2_zone_pipeline = OtbPipelineManager()
self._l2_edg_pipeline = OtbPipelineManager()
self.m_headerHandler = None
self._band_zone_map = {}
def can_read(self, plugin_name):
return plugin_name == "SENTINEL2_MUSCATE"
def generate_toa(self, listOfTOAImageFileNames,
reflectanceMultiplicationValues, working):
"""
:param listOfTOAImageFileNames: ListOfStrings
:param reflectanceMultiplicationValues: ListOfDoubles
:return:
:rtype: string
"""
if len(reflectanceMultiplicationValues) != len(
listOfTOAImageFileNames):
raise MajaDataException(
"Not the same number of band images and coefficients")
# =======> GENERATE TOA CACHING
l_ProjectionRef = self.generate_toa_caching(
listOfTOAImageFileNames, reflectanceMultiplicationValues, working)
LOGGER.debug("Caching TOA images done ...")
return l_ProjectionRef
def generate_sat_images(self, working_dir):
"""
:return:
"""
# *******************************************************************************************************
# IPSAT Sub image pipeline connection
# *******************************************************************************************************
out_concatenate = os.path.join(working_dir, "SubSatVector.tif")
param_concatenate = {
"il": self._satmasksublist,
"out": out_concatenate + ":uint8"
}
concat_app = OtbAppHandler("ConcatenateDoubleImages",
param_concatenate)
self._subsatimage = concat_app.getoutput().get("out")
self._sub_sat_pipeline.free_otb_app()
def generate_mask_rasters(self, satPixFileNames, defectivPixFileNames,
zoneMaskFileNames, noDataMaskFileNames,
p_ListOfTOABandCode, working):
"""
:param satPixFileNames: ListOfStrings
:param defectivPixFileNames: ListOfStrings
:param zoneMaskFileNames: ListOfStrings
:param noDataMaskFileNames: ListOfStrings
:param p_ListOfTOABandCode: ListOfStrings
:param working: string
:return:
"""
# *******************************************************************************************************
# Generate mask rasters by rasterizing the gml mask per L2 resolution per band
# *******************************************************************************************************
LOGGER.info("Starting GenerateMaskRasters")
l_BandsDefinitions = self._plugin.BandsDefinitions
l2Area = None
l_ListOfL2Resolution = l_BandsDefinitions.ListOfL2Resolution # ListOfStrings
l_NbL2Res = len(l_ListOfL2Resolution)
l_ListOfL1Resolution = l_BandsDefinitions.ListOfL1Resolution # ListOfStrings
l_NbL1Res = len(l_ListOfL1Resolution)
l_NbL1Band = len(defectivPixFileNames)
dtm_coarse = self._dem.ALC
# Set a constant image if the gml masks are empty
tmp_constant_filename = os.path.join(working, "Masks_const.tif")
#constant_image(dtm_coarse, 0, tmp_constant_filename, write_output=True)
# Initialize the L2 Elements
for l2res in range(l_NbL2Res):
self._l2defectmasklist.append([])
self._l2zonemasklist.append([])
self._l2satimagelist.append([])
self._l2dfpimagelist.append(None)
self._l2zoneimagelist.append(None)
# Init the coarse elements
for coarseband in p_ListOfTOABandCode:
self._satmasksublist.append(None)
self._nodatamasksublist.append(None)
self._zonemasksublist.append(None)
self._l2satmasklist.append(None)
# Loop on L1Res
for l1res in range(l_NbL1Res):
# Current resolution: "R1" or "R2" or "R3"
curL1Res = l_ListOfL1Resolution[l1res]
# Get the list of band of the current resolution
listOfL1Bands = l_BandsDefinitions.get_list_of_l1_band_code(
curL1Res)
nbL1Bands = len(listOfL1Bands)
# For each band of the current resolution
l_l2zone_pipeline = OtbPipelineManager()
firstBandIdx = l_BandsDefinitions.get_band_id_in_l1(
listOfL1Bands[-1])
# Verify if we can optimize the work if its the same file for all bands of
# the resolution and if the bands are in correct bit order
l_strDefectFileNameRef = defectivPixFileNames[firstBandIdx]
l_isDefectSameFilesForBands = True
l_strSatFileNameRef = satPixFileNames[firstBandIdx]
l_isSatSameFilesForBands = True
l_strNoDataFileNameRef = noDataMaskFileNames[firstBandIdx]
l_isNoDataSameFilesForBands = True
for l_BandIdxL1 in range(len(listOfL1Bands)):
# Get the L1 band index associated to the L2 band code
l_StrBandIdL1 = listOfL1Bands[l_BandIdxL1]
l1BandIdx = l_BandsDefinitions.get_band_id_in_l1(l_StrBandIdL1)
# Filenameverify
if l_strDefectFileNameRef != defectivPixFileNames[l1BandIdx]:
l_isDefectSameFilesForBands = False
if l_strSatFileNameRef != satPixFileNames[l1BandIdx]:
l_isSatSameFilesForBands = False
if l_strNoDataFileNameRef != noDataMaskFileNames[l1BandIdx]:
l_isNoDataSameFilesForBands = False
LOGGER.debug("l_isDefectSameFilesForBands = " +
str(l_isDefectSameFilesForBands))
LOGGER.debug("l_isSatSameFilesForBands = " +
str(l_isSatSameFilesForBands))
LOGGER.debug("l_isNoDataSameFilesForBands = " +
str(l_isSatSameFilesForBands))
# Defective PIX (only in L2 resolution)
if l_isDefectSameFilesForBands:
if curL1Res in l_ListOfL2Resolution:
l_StrBandIdL1 = listOfL1Bands[0]
l1BandIdx = l_BandsDefinitions.get_band_id_in_l1(
l_StrBandIdL1)
LOGGER.debug("l_StrBandIdL1 : ")
LOGGER.debug(l_StrBandIdL1)
self._l2piximagelist.append(
defectivPixFileNames[l1BandIdx])
dfp_mask = os.path.join(
working, "L1_DFP_Masks_{}.tif".format(curL1Res))
param_bintovec_dfp = {
"im": defectivPixFileNames[l1BandIdx],
"out": dfp_mask + ":uint8",
"nbcomp": nbL1Bands
}
dfp_mask_app = OtbAppHandler("BinaryToVector",
param_bintovec_dfp,
write_output=False)
self._l2_dfp_pipeline.add_otb_app(dfp_mask_app)
self._l2dfpimagelist[l_ListOfL2Resolution.index(
curL1Res)] = dfp_mask_app.getoutput().get("out")
else:
raise MajaExceptionPluginSentinel2Muscate(
"Product format not supported : not the same file for band on PIX"
)
# SAT Mask generation
if l_isSatSameFilesForBands:
# Sat Masks generation
l_StrBandIdL1 = listOfL1Bands[0]
l1BandIdx = l_BandsDefinitions.get_band_id_in_l1(l_StrBandIdL1)
l_SATFFilename = satPixFileNames[l1BandIdx]
sat_mask = os.path.join(working,
"L1_SAT_Masks_{}.tif".format(curL1Res))
param_bintovec_dfp = {
"im": l_SATFFilename,
"out": sat_mask + ":uint8",
"nbcomp": nbL1Bands
}
sat_mask_app = OtbAppHandler("BinaryToVector",
param_bintovec_dfp,
write_output=True)
# self._l2_sat_pipeline.add_otb_app(sat_mask_app)
if curL1Res in l_ListOfL2Resolution:
self._l2satmasklist[l_ListOfL2Resolution.index(
curL1Res)] = sat_mask_app.getoutput().get("out")
tmp_sat_resample = os.path.join(
working,
"tmp_extract_roi_sat_resample_{}.tif".format(curL1Res))
sat_resamp_app = resample(sat_mask_app.getoutput().get("out"),
dtm_coarse,
tmp_sat_resample,
OtbResampleType.LINEAR,
write_output=False)
self._sub_sat_pipeline.add_otb_app(sat_resamp_app)
for l1band in listOfL1Bands:
l1BandIdx = l_BandsDefinitions.get_band_id_in_l1(l1band)
tmp_sat_roi = os.path.join(
working, "tmp_extract_roi_sat_{}.tif".format(l1band))
tmp_sat_roi_app = extract_roi(
sat_resamp_app.getoutput().get("out"), [
self.m_headerHandler.get_l1_sat_image_index(
l1BandIdx) - 1
],
tmp_sat_roi,
write_output=False)
self._sub_sat_pipeline.add_otb_app(tmp_sat_roi_app)
self._satmasksublist[
l1BandIdx] = tmp_sat_roi_app.getoutput().get("out")
else:
raise MajaExceptionPluginSentinel2Muscate(
"Product format not supported : not the same file for band on SAT"
)
# No_data mask at L2 coarse
if l_isNoDataSameFilesForBands:
ndt_mask = os.path.join(
working, "L2_NDT_VecMasks_{}.tif".format(curL1Res))
l_StrBandIdL1 = listOfL1Bands[0]
l1BandIdx = l_BandsDefinitions.get_band_id_in_l1(l_StrBandIdL1)
l_NoDataFilename = noDataMaskFileNames[l1BandIdx]
param_bintovec_ndt = {
"im": l_NoDataFilename,
"out": ndt_mask + ":uint8",
"nbcomp": nbL1Bands
}
ndt_mask_app = OtbAppHandler("BinaryToVector",
param_bintovec_ndt,
write_output=False)
self._subedg_pipeline.add_otb_app(ndt_mask_app)
tmp_ndt_resample = os.path.join(
working,
"tmp_extract_roi_ndt_resample_{}.tif".format(curL1Res))
ndt_resamp_app = resample(ndt_mask_app.getoutput().get("out"),
dtm_coarse,
tmp_ndt_resample,
OtbResampleType.LINEAR,
write_output=False)
self._subedg_pipeline.add_otb_app(ndt_resamp_app)
for l1band in listOfL1Bands:
l1BandIdx = l_BandsDefinitions.get_band_id_in_l1(l1band)
tmp_ndt_roi = os.path.join(
working, "tmp_extract_roi_ndt_{}.tif".format(l1band))
tmp_ndt_roi_app = extract_roi(
ndt_resamp_app.getoutput().get("out"), [
self.m_headerHandler.get_l1_ndt_image_index(
l1BandIdx) - 1
],
tmp_ndt_roi,
write_output=False)
self._subedg_pipeline.add_otb_app(tmp_ndt_roi_app)
self._nodatamasksublist[
l1BandIdx] = tmp_ndt_roi_app.getoutput().get("out")
else:
raise MajaExceptionPluginSentinel2Muscate(
"Product format not supported : not the same file for band on NoData"
)
# Detectors FootPrint Masks Generation
# Get all the detectors files realted to this resolution
# Test if they all refers to the same files
tmp_res_det_filenames = None
all_same_det_for_bands = True
for l_BandIdxL1 in range(len(listOfL1Bands)):
# Get the L1 band index associated to the L2 band code
l_StrBandIdL1 = listOfL1Bands[l_BandIdxL1]
l1BandIdx = l_BandsDefinitions.get_band_id_in_l1(l_StrBandIdL1)
LOGGER.debug(
"Sentinel2MuscateL1ImageFileReader::GenerateMaskRasters: CurrentResol = %s, reading the "
"BandId L1 (associated) <%s> with index <%s>.", curL1Res,
l_StrBandIdL1, l1BandIdx)
# Detectors FootPrint Masks
l_bandDetFnames = zoneMaskFileNames[l1BandIdx]
if tmp_res_det_filenames is not None:
if len(tmp_res_det_filenames) != len(l_bandDetFnames):
all_same_det_for_bands = False
break
else:
tmp_res_det_filenames = l_bandDetFnames
#Construct file and det number list
tmp_list_of_detf_filenames = []
tmp_list_of_detf_num = []
tmp_list_of_band_idx = []
if all_same_det_for_bands:
l_ListOfZone = self.m_headerHandler.get_list_of_zones(
listOfL1Bands[0])
nbDetector = len(l_ListOfZone)
# For each detector of the current band
for det in range(nbDetector):
tmp_list_of_detf_filenames.append(
tmp_res_det_filenames[l_ListOfZone[det]])
tmp_list_of_detf_num.append(str(int(l_ListOfZone[det])))
else:
#some bands are not in all det rare case
tmp_det_info_map = {}
for l_BandIdxL1 in range(len(listOfL1Bands)):
# Get the L1 band index associated to the L2 band code
l_StrBandIdL1 = listOfL1Bands[l_BandIdxL1]
l1BandIdx = l_BandsDefinitions.get_band_id_in_l1(
l_StrBandIdL1)
LOGGER.debug(
"Sentinel2MuscateL1ImageFileReader::GenerateMaskRasters: CurrentResol = %s, reading the "
"BandId L1 (associated) <%s> with index <%s>.",
curL1Res, l_StrBandIdL1, l1BandIdx)
# Detectors FootPrint Masks
l_bandDetFnames = zoneMaskFileNames[l1BandIdx]
l_ListOfZone = self.m_headerHandler.get_list_of_zones(
l_StrBandIdL1)
nbDetector = len(l_ListOfZone)
# For each detector of the current band
for det in range(nbDetector):
det_num = l_ListOfZone[det]
if det_num not in tmp_det_info_map.keys():
tmp_det_info_map[det_num] = (
zoneMaskFileNames[l1BandIdx][det_num],
[0] * len(listOfL1Bands))
tmp_det_info_map[det_num][1][l_BandIdxL1] = \
self.m_headerHandler.get_l1_dtf_image_index(l1BandIdx, det_num)[0]
#once the info map is done
for det in tmp_det_info_map.keys():
det_info = tmp_det_info_map[det]
tmp_list_of_detf_filenames.append(det_info[0])
tmp_list_of_detf_num.append(str(int(det)))
for x in det_info[1]:
tmp_list_of_band_idx.append(str(x))
#create params
detf_mask = os.path.join(working,
"L1_DETF_Masks_{}.tif".format(curL1Res))
param_dispacth_zone = {
"il": tmp_list_of_detf_filenames,
"out": detf_mask + ':uint8',
"nbcomp": len(listOfL1Bands),
"outvals": tmp_list_of_detf_num
}
#handle not all det/band case
if not all_same_det_for_bands:
param_dispacth_zone["outindexes"] = tmp_list_of_band_idx
#Call the app
dispatch_app = OtbAppHandler("DispatchZonesToVector",
param_dispacth_zone,
write_output=True)
#This is the L2 output
if curL1Res in l_ListOfL2Resolution:
self._l2zoneimagelist[l_ListOfL2Resolution.index(
curL1Res)] = dispatch_app.getoutput().get("out")
#extract the coarse bands
for l1band in listOfL1Bands:
l1BandIdx = l_BandsDefinitions.get_band_id_in_l1(l1band)
tmp_zone_roi = os.path.join(
working, "tmp_extract_roi_ndt_{}.tif".format(l1band))
tmp_zone_roi_app = extract_roi(
dispatch_app.getoutput().get("out"), [
self.m_headerHandler.get_l1_ndt_image_index(l1BandIdx)
- 1
],
tmp_zone_roi,
write_output=False)
self._subedg_pipeline.add_otb_app(tmp_zone_roi_app)
tmp_zone_with_nodata = os.path.join(
working, "tmp_zone_with_nodata_{}".format(curL1Res))
masterExpr = "( im1b1 > 0 ) ? im1b1 : -10000"
param_bandmath_zone = {
"il": [tmp_zone_roi_app.getoutput().get("out")],
"exp": masterExpr,
"out": tmp_zone_with_nodata
}
zone_thresh_bandmath_app = OtbAppHandler("BandMathDouble",
param_bandmath_zone,
write_output=False)
# LAIG-FA-MAC-1652-CNES : probleme reech detecteur en unsigned char : detecteur 1 devient 0.....
tmp_zone_resample = os.path.join(
working,
"tmp_extract_roi_zone_resample_{}.tif".format(l1BandIdx))
zone_resample_app = resample(
zone_thresh_bandmath_app.getoutput().get("out"),
dtm_coarse,
tmp_zone_resample,
OtbResampleType.LINEAR,
write_output=False)
# threshold everything negative to 0
Thresholdexpr = "im1b1 > 0 ? im1b1 : 0"
tmp_zone_threshold = os.path.join(
working, "tmp_zone_threshold_{}".format(l1BandIdx))
param_bandmath_zone_threshold = {
"il": [zone_resample_app.getoutput().get("out")],
"exp": Thresholdexpr,
"out": tmp_zone_threshold
}
zone_bandmath_threshold_app = OtbAppHandler(
"BandMathDouble",
param_bandmath_zone_threshold,
write_output=False)
# Rounding
tmp_zone_round = os.path.join(
working, "tmp_zone_round_{}".format(l1BandIdx))
param_bandmath_zone_round = {
"im": zone_bandmath_threshold_app.getoutput().get("out"),
"out": tmp_zone_round
}
zone_round_app = OtbAppHandler("RoundImage",
param_bandmath_zone_round,
write_output=True)
# Add to the official output
self._zonemasksublist[l1BandIdx] = zone_round_app.getoutput(
).get("out")
# Log current loop
LOGGER.debug("band loop: " + str(l1BandIdx + 1) + " / " +
str(nbL1Bands) + " (" + curL1Res + ")")
# band loop
LOGGER.debug("band loop: END")
self._l2_detf_pipeline.free_otb_app()
# L2 Zone mask pipeline connection
# if curL1Res in l_ListOfL2Resolution:
# l2_zone_image = os.path.join(working, "l2_zone_mask_{}.tif".format(curL1Res))
# param_l2zone_concatenate = {"il": self._l2zonemasklist[l_ListOfL2Resolution.index(curL1Res)],
# "out": l2_zone_image
# }
# l2_zone_app = OtbAppHandler("ConcatenateImages", param_l2zone_concatenate,write_output=True)
# self._l2zoneimagelist[l_ListOfL2Resolution.index(curL1Res)] = l2_zone_app.getoutput().get("out")
# end res loop
def GetViewingGrids(self, band, nbL1Bands, vieHRef, satFilename,
zoneFilenames, nodataFilename, listOfZone,
view_angle_col_step, view_angle_row_step,
viewing_angles_zenith, viewing_angles_azimuth,
working):
LOGGER.debug("GetViewingGrids - Band: " + band)
dtm_coarse = self._dem.ALC
l_BandsDefinitions = self._plugin.BandsDefinitions
l1BandIdx = l_BandsDefinitions.get_band_id_in_l1(band)
# ---- Viewing angle Grid --------------------------------------------------------------------
l_VieAnglesGridList = []
l_nbDetectors = len(viewing_angles_azimuth)
# Detector loop
LOGGER.debug("For each detectors (nb=%s) ...", l_nbDetectors)
view_resamp_pipeline = OtbPipelineManager()
for detId in range(len(viewing_angles_azimuth)):
# Generate an image with the list of viewing angle values set in the header file
det = listOfZone[detId]
LOGGER.debug("ZenithViewingAngles for band " + band + " and det " +
det + " , nb values: " +
str(len(viewing_angles_zenith[detId])))
LOGGER.debug(viewing_angles_zenith[detId])
LOGGER.debug("AzimuthViewingAngles " + band + " and det " + det +
" nb values: " +
str(len(viewing_angles_azimuth[detId])))
LOGGER.debug(viewing_angles_azimuth[detId])
output_filename = "/tmp/internal_angles_test_{}_{}.xml".format(
band, det)
LOGGER.debug("Angles output_filename : %s", output_filename)
writer = MajaInternalXmlInputAngles(viewing_angles_zenith[detId],
viewing_angles_azimuth[detId],
view_angle_col_step,
view_angle_row_step,
output_filename)
writer.write()
if not os.path.exists(output_filename):
raise MajaExceptionPluginSentinel2Muscate(
"File does not exist " + output_filename)
viewing_grid_filename = os.path.join(
working, "viewing_grid_{}_{}.tif".format(det, band))
# angle_list_to_image()
viewing_angle_app = angle_list_to_image(dtm_coarse,
output_filename,
viewing_grid_filename,
extrapolation=False,
write_output=False)
view_resamp_pipeline.add_otb_app(viewing_angle_app)
# Expand at L2Coarse.
viewing_grid_resamp_filename = os.path.join(
working, "viewing_grid_resamp_{}_{}.tif".format(detId, band))
viewing_grid_resamp_app = resample(
viewing_angle_app.getoutput().get("out"),
dtm_coarse,
viewing_grid_resamp_filename,
OtbResampleType.LINEAR,
write_output=False)
view_resamp_pipeline.add_otb_app(viewing_grid_resamp_app)
# add images in a list
l_VieAnglesGridList.append(
viewing_grid_resamp_app.getoutput().get("out"))
# end detector loop
# Generate the angle images using the zone (detector) mask
LOGGER.debug(
"Start ConcatenatePerZoneVectorImageFilter for band id [" + band +
"]...")
# Concatenate all the detectors
viewing_concat_filename = os.path.join(
working, "viewing_concat_{}.tif".format(band))
param_concat_perzone = {
"mask": self._zonemasksublist[l1BandIdx],
"il": l_VieAnglesGridList,
"zonelist": listOfZone,
"out": viewing_concat_filename
}
concat_perzone_app = OtbAppHandler("ConcatenatePerZone",
param_concat_perzone,
write_output=False)
# Multiply by reference altitude
viewing_grid_mult_filename = os.path.join(
working, "viewing_grid_mult_{}.tif".format(band))
param_scaled_solar = {
"im": concat_perzone_app.getoutput().get("out"),
"coef": float(vieHRef),
"out": viewing_grid_mult_filename
}
view_scale_app = OtbAppHandler("MultiplyByScalar",
param_scaled_solar,
write_output=True)
self._vieimagelist.append(view_scale_app.getoutput().get("out"))
l_nbZones = len(listOfZone)
LOGGER.debug("Start Loop for Zone (nb=" + str(l_nbZones) + ")...")
for d in range(l_nbZones):
l_zone = listOfZone[d]
# -----------------------------------------------------------------------------------
# Compute average values of zenithal and azimuthal angles grid per zone (detector in level1B)
# -----------------------------------------------------------------------------------
# VAP Reader connection (from ATB)
tmp_azi = os.path.join(working,
"tmp_azi_{}_{}.tif".format(band, l_zone))
tmp_azi_image = extract_roi(l_VieAnglesGridList[d], [1],
tmp_azi,
write_output=False)
param_stats = {
"im": tmp_azi_image.getoutput().get("out"),
"exclude": 1,
"mask": self._zonemasksublist[l1BandIdx],
"maskforeground": int(l_zone)
}
l2_stat = OtbAppHandler("Stats", param_stats)
azi_mean = l2_stat.getoutput().get("mean")
tmp_zen = os.path.join(working,
"tmp_zen_{}_{}.tif".format(band, l_zone))
tmp_zen_app = extract_roi(l_VieAnglesGridList[d], [0],
tmp_zen,
write_output=False)
param_stats = {
"im": tmp_zen_app.getoutput().get("out"),
"exclude": 1,
"mask": self._zonemasksublist[l1BandIdx],
"maskforeground": int(l_zone)
}
l2_stat = OtbAppHandler("Stats", param_stats)
zen_mean = l2_stat.getoutput().get("mean")
tmp_mean = (zen_mean, azi_mean)
l_ViewingAngleMean = grid_to_angle(tmp_mean)
l_ViewingAngleMeanDeg = (l_ViewingAngleMean[0] * 180.0 / math.pi,
l_ViewingAngleMean[1] * 180.0 / math.pi)
# Add a vector to mean maps
if l_zone not in self._meanZenithMap:
self._meanZenithMap[listOfZone[d]] = []
if l_zone not in self._meanAzimuthMap:
self._meanAzimuthMap[listOfZone[d]] = []
self._meanZenithMap[listOfZone[d]].append(
str(l_ViewingAngleMeanDeg[0]))
self._meanAzimuthMap[listOfZone[d]].append(
str(l_ViewingAngleMeanDeg[1]))
LOGGER.debug(" For BandId[" + str(band) + "], zone [" +
str(listOfZone[d]) + "] . Mean 'GRID View angles'=" +
str(tmp_mean) + " . Mean 'View angles'=" +
str(l_ViewingAngleMean[0]) + ":" +
str(l_ViewingAngleMean[1]) + " rad. ou " +
str(l_ViewingAngleMeanDeg[0]) + ":" +
str(l_ViewingAngleMeanDeg[1]) + " deg.")
LOGGER.debug("Start Loop for Zone done.")
view_resamp_pipeline.free_otb_app()
# Can read method
def read(self, product_info, app_handler, l2comm, dem, pReadL1Mode):
"""product_info,plugin, l2comm,mode
:param product_info: L1ImageInformationsBase
:param pReadL1Mode: ReadL1ModeType
:return:
"""
LOGGER.info("Start Sentinel2Muscate L1 ImageFileReader ...")
product_filename = product_info.HeaderFilename
LOGGER.info(
"Start Sentinel2Muscate L1 ImageFileReader with filename : " +
product_filename)
working_dir = app_handler.get_directory_manager(
).get_temporary_directory("L1Read_", do_always_remove=True)
self._plugin.initialize(app_handler)
self._GIPPL2COMMHandler = l2comm
LOGGER.info("Start Sentinel2Muscate L1 ImageFileReader ...")
self._ReadL1Mode = pReadL1Mode
self._dem = dem
# --------------------------------------
# Check the extension of th efile. For Muscate, must be .XML (in upper case)
IsValidSatellite = (product_info.HeaderHandler is not None)
if not IsValidSatellite:
raise MajaExceptionPluginSentinel2Muscate(
"The file <{}> is not a valid Sentinel2 L1 product.".format(
product_filename))
self.m_headerHandler = product_info.HeaderHandler
# Get satellite name
self.m_Satellite = product_info.Satellite
# --------------------------------------
# Load the header tile file in the xml tile handler to read tile information
xmlTileFilename = product_info.xmlTileFilename
l_BandsDefinitions = self._plugin.BandsDefinitions # BandsDefinitions
# TOA & masks Reader connection
l_ListOfTOAImageFileNames = product_info.HeaderHandler.get_list_of_toa_image_filenames(
)
l_SatPixFileNames = product_info.HeaderHandler.get_list_of_l1_sat_image_filenames(
)
l_DefectivPixFileNames = product_info.HeaderHandler.get_list_of_defective_pixel_image_filenames(
)
l_ZoneMaskFileNames = product_info.HeaderHandler.get_map_list_of_detector_footprint_image_filenames(
)
l_NoDataMaskFileNames = product_info.HeaderHandler.get_list_of_l1_ndt_image_filenames(
)
l_ListOfTOABandCode = product_info.HeaderHandler.get_list_of_band_code(
) # ListOfStrings
l_NbBand = len(l_ListOfTOAImageFileNames) # int
l_L1NoData = product_info.L1NoData
l_ReflectanceQuantificationValue = product_info.ReflectanceQuantification
l_RealL1NoData = l_L1NoData * l_ReflectanceQuantificationValue # RealNoDataType
if (len(l_ListOfTOABandCode) != len(l_ListOfTOAImageFileNames)) or (
len(l_ListOfTOABandCode) != len(l_SatPixFileNames)) or (
len(l_ListOfTOABandCode) != len(l_DefectivPixFileNames)):
raise MajaExceptionPluginSentinel2Muscate(
"Sentinel2L1ImageFileReader.CanRead: Internal error: The number of image file, sat file and band code are different !"
)
# =======> GENERATE TOA
# Get calibration coeffs if activated
l_reflectanceMultiplicationValues = [] # ListOfDoubles
if xml_tools.as_bool(l2comm.get_value("CalAdjustOption")):
l_factors = xml_tools.as_float_list(
l2comm.get_value("CalAdjustFactor"))
if len(l_factors) != len(l_ListOfTOABandCode):
raise MajaDataException(
"Not the same number of Calibration coeffs than L1 bands ( {}, {} )"
.format(len(l_factors), len(l_ListOfTOABandCode)))
for i in range(len(l_ListOfTOABandCode)):
l_reflectanceMultiplicationValues.append(
l_ReflectanceQuantificationValue * l_factors[i])
else:
for i in range(len(l_ListOfTOABandCode)):
l_reflectanceMultiplicationValues.append(
l_ReflectanceQuantificationValue)
l_ProjectionRef = self._dem.ProjRef
self.generate_toa(l_ListOfTOAImageFileNames,
l_reflectanceMultiplicationValues,
working_dir) # string
# START READ L1 for ALGORITHMS
if pReadL1Mode == ReadL1Mode.READ_L1_MODE_FOR_ALGORITHMS:
# =======> GENERATE TOA SUB IMAGES AT L2 COARSE RESOLUTION
LOGGER.debug("Start SubSampling ...")
self.generate_toa_sub_images(working_dir)
LOGGER.debug("Start SubSampling done.")
# the gml mask per L2 resolution per band
self.generate_mask_rasters(l_SatPixFileNames,
l_DefectivPixFileNames,
l_ZoneMaskFileNames,
l_NoDataMaskFileNames,
l_ListOfTOABandCode, working_dir)
LOGGER.debug("Filenames Mask Rasters : ")
LOGGER.debug(l_SatPixFileNames)
LOGGER.debug(l_DefectivPixFileNames)
LOGGER.debug(l_ZoneMaskFileNames)
LOGGER.debug(l_NoDataMaskFileNames)
LOGGER.debug(l_ListOfTOABandCode)
# Get the ref altitude
l_CurrentConfigUserCamera = self._plugin.ConfigUserCamera
grid_ref_alt = l_CurrentConfigUserCamera.get_Algorithms(
).get_GRID_Reference_Altitudes()
l_VIEHRef = grid_ref_alt.get_VIEHRef() # double
l_SOLH1 = grid_ref_alt.get_SOLH1() # double
# IPSOL Sub image pipeline connection
LOGGER.debug("Start IPSOL SubSampling ...")
sun_angle_col_step = int(
product_info.HeaderHandler.get_string_value_of(
"SunAngleColStep"))
sun_angle_row_step = int(
product_info.HeaderHandler.get_string_value_of(
"SunAngleRowStep"))
# ATTENTION : * -1 for compatibility negative spacing
LOGGER.debug(
"Angle Resolution Spacing computed in 5000 resolution: " +
str(sun_angle_col_step) + " , " + str(sun_angle_row_step))
l_zenithSolarAngles = product_info.HeaderHandler.get_sun_zenithal_angles(
)
l_azimuthSolarAngles = product_info.HeaderHandler.get_sun_azimuthal_angles(
)
LOGGER.debug("ZenithSolarAngles nb values: " +
str(len(l_zenithSolarAngles)))
LOGGER.debug("AzimuthSolarAngles nb values: " +
str(len(l_azimuthSolarAngles)))
l_solarAngleFile = app_handler.get_directory_manager(
).get_temporary_file("sun_angles_") + ".xml"
LOGGER.debug("Angles output_filename : %s", l_solarAngleFile)
writer = MajaInternalXmlInputAngles(l_zenithSolarAngles,
l_azimuthSolarAngles,
sun_angle_col_step,
sun_angle_row_step,
l_solarAngleFile)
writer.write()
# =======> GENERATE SOLAR ANGLE GRIDS
self.get_solar_grids(self._dem.ALC, l_solarAngleFile, l_SOLH1,
working_dir)
LOGGER.debug("Start IPSOL SubSampling done.")
# IPVIE Sub image pipeline connection
LOGGER.debug("Start IPVIE SubSampling ...")
vie_angle_col_step = int(
product_info.HeaderHandler.get_string_value_of(
"SunAngleColStep"))
vie_angle_row_step = int(
product_info.HeaderHandler.get_string_value_of(
"SunAngleRowStep"))
LOGGER.debug("For each band ...")
for l_band in l_ListOfTOABandCode:
l1BandIdx = l_BandsDefinitions.get_band_id_in_l1(l_band)
# Get the list of detectors (index)
l_ListOfZone = product_info.HeaderHandler.get_list_of_zones(
l_band)
# Get the list of viewing angles in the header file of the tile
l_zenithVieAngles = product_info.HeaderHandler.get_viewing_zenithal_angles(
l_band)
l_azimuthVieAngles = product_info.HeaderHandler.get_viewing_azimuthal_angles(
l_band)
if len(l_zenithVieAngles) != len(l_azimuthVieAngles):
raise MajaExceptionPluginSentinel2Muscate(
"The number of detector for viewing zenithal angles and viewing azimuthal angles is different or null !"
)
# =======> GENERATE VIEWING ANGLE GRIDS
self.GetViewingGrids(l_band, l_NbBand, l_VIEHRef,
l_SatPixFileNames[l1BandIdx],
l_ZoneMaskFileNames[l1BandIdx],
l_NoDataMaskFileNames[l1BandIdx],
l_ListOfZone, vie_angle_col_step,
vie_angle_row_step, l_zenithVieAngles,
l_azimuthVieAngles, working_dir)
# end loop nbBand
LOGGER.debug("Start IPVIE SubSampling done.")
# To check mean angle values
# TODO
# IPEDGSub and L2EDG pipeline connection
# =======> GENERATE EDG IMAGES
self.generate_edg_images(working_dir)
# L2 TOA image pipeline connection
# =======> GENERATE L2 TOA IMAGES
self.generate_l2_toa_images(working_dir)
# IPSAT Sub and L2SAT image pipeline connection
# =======> GENERATE SAT IMAGES
self.generate_sat_images(working_dir)
# CLA image pipeline connection
# =======> GENERATE CLA IMAGES
self.generate_cla_images(l_RealL1NoData, working_dir)
# Fill the datas
self.dict_of_vals["IPEDGSubOutput"] = self._edgsubmask
self.dict_of_vals["SOL1Image"] = self._sol1image
l_DTMBandCode = xml_tools.as_string_list(
l2comm.get_value("DTMViewingDirectionBandCode"))[0]
l_DTMBandIdx = self._plugin.BandsDefinitions.get_band_id_in_l2_coarse(
l_DTMBandCode)
self.dict_of_vals["DTMVIEImage"] = self._vieimagelist[l_DTMBandIdx]
LOGGER.debug("l_DTMBandIdx : ")
LOGGER.debug(l_DTMBandIdx)
self.dict_of_vals["IPTOASubOutput"] = self._sub_toa
self.dict_of_vals["L2TOAImageList"] = self._l2toaimagelist
self.dict_of_vals["ViewingZenithMeanMap"] = self._meanZenithMap
self.dict_of_vals["ViewingAzimuthMeanMap"] = self._meanAzimuthMap
self.dict_of_vals["CLAImage"] = self._claimage
self.dict_of_vals["IPSATSubOutput"] = self._subsatimage
l_CLDBandCode = l2comm.get_value("CLDViewingDirectionBandCode")
l_CLDBandIdx = self._plugin.BandsDefinitions.get_band_id_in_l2_coarse(
l_CLDBandCode)
LOGGER.debug("l_CLDBandIdx: ")
LOGGER.debug(l_CLDBandIdx)
self.dict_of_vals["ShadowVIEImage"] = self._vieimagelist[
l_CLDBandIdx]
l_CirrusBandCode = l2comm.get_value("CirrusBandCode")
l_CirrusBandIdx = self._plugin.BandsDefinitions.get_band_id_in_l2_coarse(
l_CirrusBandCode)
LOGGER.debug("l_CirrusBandIdx: ")
LOGGER.debug(l_CirrusBandIdx)
self.dict_of_vals["L1TOACirrusImage"] = self._toa_scalar_list[
l_CirrusBandIdx]
self.dict_of_vals["AngleZoneMaskList"] = self._l2zoneimagelist
self.dict_of_vals["L2EDGOutputList"] = self._l2edgmasklist
self.dict_of_vals["L2SATImageList"] = self._l2satmasklist
self.dict_of_vals["L2PIXImageList"] = self._l2piximagelist
self.dict_of_vals["L2DFPImageList"] = self._l2dfpimagelist
def GetViewingGrids(self, band, nbL1Bands, vieHRef, satFilename,
zoneFilenames, nodataFilename, listOfZone,
view_angle_col_step, view_angle_row_step,
viewing_angles_zenith, viewing_angles_azimuth,
working):
LOGGER.debug("GetViewingGrids - Band: " + band)
dtm_coarse = self._dem.ALC
l_BandsDefinitions = self._plugin.BandsDefinitions
l1BandIdx = l_BandsDefinitions.get_band_id_in_l1(band)
# ---- Viewing angle Grid --------------------------------------------------------------------
l_VieAnglesGridList = []
l_nbDetectors = len(viewing_angles_azimuth)
# Detector loop
LOGGER.debug("For each detectors (nb=%s) ...", l_nbDetectors)
view_resamp_pipeline = OtbPipelineManager()
for detId in range(len(viewing_angles_azimuth)):
# Generate an image with the list of viewing angle values set in the header file
det = listOfZone[detId]
LOGGER.debug("ZenithViewingAngles for band " + band + " and det " +
det + " , nb values: " +
str(len(viewing_angles_zenith[detId])))
LOGGER.debug(viewing_angles_zenith[detId])
LOGGER.debug("AzimuthViewingAngles " + band + " and det " + det +
" nb values: " +
str(len(viewing_angles_azimuth[detId])))
LOGGER.debug(viewing_angles_azimuth[detId])
output_filename = "/tmp/internal_angles_test_{}_{}.xml".format(
band, det)
LOGGER.debug("Angles output_filename : %s", output_filename)
writer = MajaInternalXmlInputAngles(viewing_angles_zenith[detId],
viewing_angles_azimuth[detId],
view_angle_col_step,
view_angle_row_step,
output_filename)
writer.write()
if not os.path.exists(output_filename):
raise MajaExceptionPluginSentinel2Muscate(
"File does not exist " + output_filename)
viewing_grid_filename = os.path.join(
working, "viewing_grid_{}_{}.tif".format(det, band))
# angle_list_to_image()
viewing_angle_app = angle_list_to_image(dtm_coarse,
output_filename,
viewing_grid_filename,
extrapolation=False,
write_output=False)
view_resamp_pipeline.add_otb_app(viewing_angle_app)
# Expand at L2Coarse.
viewing_grid_resamp_filename = os.path.join(
working, "viewing_grid_resamp_{}_{}.tif".format(detId, band))
viewing_grid_resamp_app = resample(
viewing_angle_app.getoutput().get("out"),
dtm_coarse,
viewing_grid_resamp_filename,
OtbResampleType.LINEAR,
write_output=False)
view_resamp_pipeline.add_otb_app(viewing_grid_resamp_app)
# add images in a list
l_VieAnglesGridList.append(
viewing_grid_resamp_app.getoutput().get("out"))
# end detector loop
# Generate the angle images using the zone (detector) mask
LOGGER.debug(
"Start ConcatenatePerZoneVectorImageFilter for band id [" + band +
"]...")
# Concatenate all the detectors
viewing_concat_filename = os.path.join(
working, "viewing_concat_{}.tif".format(band))
param_concat_perzone = {
"mask": self._zonemasksublist[l1BandIdx],
"il": l_VieAnglesGridList,
"zonelist": listOfZone,
"out": viewing_concat_filename
}
concat_perzone_app = OtbAppHandler("ConcatenatePerZone",
param_concat_perzone,
write_output=False)
# Multiply by reference altitude
viewing_grid_mult_filename = os.path.join(
working, "viewing_grid_mult_{}.tif".format(band))
param_scaled_solar = {
"im": concat_perzone_app.getoutput().get("out"),
"coef": float(vieHRef),
"out": viewing_grid_mult_filename
}
view_scale_app = OtbAppHandler("MultiplyByScalar",
param_scaled_solar,
write_output=True)
self._vieimagelist.append(view_scale_app.getoutput().get("out"))
l_nbZones = len(listOfZone)
LOGGER.debug("Start Loop for Zone (nb=" + str(l_nbZones) + ")...")
for d in range(l_nbZones):
l_zone = listOfZone[d]
# -----------------------------------------------------------------------------------
# Compute average values of zenithal and azimuthal angles grid per zone (detector in level1B)
# -----------------------------------------------------------------------------------
# VAP Reader connection (from ATB)
tmp_azi = os.path.join(working,
"tmp_azi_{}_{}.tif".format(band, l_zone))
tmp_azi_image = extract_roi(l_VieAnglesGridList[d], [1],
tmp_azi,
write_output=False)
param_stats = {
"im": tmp_azi_image.getoutput().get("out"),
"exclude": 1,
"mask": self._zonemasksublist[l1BandIdx],
"maskforeground": int(l_zone)
}
l2_stat = OtbAppHandler("Stats", param_stats)
azi_mean = l2_stat.getoutput().get("mean")
tmp_zen = os.path.join(working,
"tmp_zen_{}_{}.tif".format(band, l_zone))
tmp_zen_app = extract_roi(l_VieAnglesGridList[d], [0],
tmp_zen,
write_output=False)
param_stats = {
"im": tmp_zen_app.getoutput().get("out"),
"exclude": 1,
"mask": self._zonemasksublist[l1BandIdx],
"maskforeground": int(l_zone)
}
l2_stat = OtbAppHandler("Stats", param_stats)
zen_mean = l2_stat.getoutput().get("mean")
tmp_mean = (zen_mean, azi_mean)
l_ViewingAngleMean = grid_to_angle(tmp_mean)
l_ViewingAngleMeanDeg = (l_ViewingAngleMean[0] * 180.0 / math.pi,
l_ViewingAngleMean[1] * 180.0 / math.pi)
# Add a vector to mean maps
if l_zone not in self._meanZenithMap:
self._meanZenithMap[listOfZone[d]] = []
if l_zone not in self._meanAzimuthMap:
self._meanAzimuthMap[listOfZone[d]] = []
self._meanZenithMap[listOfZone[d]].append(
str(l_ViewingAngleMeanDeg[0]))
self._meanAzimuthMap[listOfZone[d]].append(
str(l_ViewingAngleMeanDeg[1]))
LOGGER.debug(" For BandId[" + str(band) + "], zone [" +
str(listOfZone[d]) + "] . Mean 'GRID View angles'=" +
str(tmp_mean) + " . Mean 'View angles'=" +
str(l_ViewingAngleMean[0]) + ":" +
str(l_ViewingAngleMean[1]) + " rad. ou " +
str(l_ViewingAngleMeanDeg[0]) + ":" +
str(l_ViewingAngleMeanDeg[1]) + " deg.")
LOGGER.debug("Start Loop for Zone done.")
view_resamp_pipeline.free_otb_app()
class MajaSlopeCorrection(MajaModule):
"""
classdocs
"""
NAME = "SlopeCorrection"
def __init__(self):
"""
Constructor
"""
super(MajaSlopeCorrection, self).__init__()
self.in_keys_to_check = [
"Params.Caching", "AppHandler", "Plugin", "L1Reader", "L2COMM",
"DEM", "L2DIRT", "L2DIFT", "L1Info"
]
self.out_keys_to_check = ["AOT_Sub"]
self.out_keys_provided = ["FRE_List", "STL_List", "TGS_List"]
self._l2_app_pipeline = OtbPipelineManager()
def run(self, dict_of_input, dict_of_output):
LOGGER.info("Slope Correction start")
slope_working = dict_of_input.get(
"AppHandler").get_directory_manager().get_temporary_directory(
"SlopeCorrProc_", do_always_remove=True)
caching = dict_of_input.get("Params").get("Caching")
# Compute transmission
tdif_image = os.path.join(slope_working, "tdif_sub.tif")
tdir_image = os.path.join(slope_working, "tdir_sub.tif")
param_interpolate = {
"luttdir":
dict_of_input.get("L2DIRT"),
"luttdif":
dict_of_input.get("L2DIFT"),
"aot":
dict_of_output["AOT_Sub"],
"dtm":
dict_of_input.get("DEM").ALC,
"edg":
dict_of_input.get("L1Reader").get_value("IPEDGSubOutput"),
"thetas":
float(dict_of_input.get("L1Info").SolarAngle["sun_zenith_angle"]),
"tdir":
tdir_image,
"tdif":
tdif_image
}
interpolate_app = OtbAppHandler("ComputeTransmission",
param_interpolate,
write_output=True)
# Compute env corr for each l2 resolution
bands_definition = dict_of_input.get("Plugin").BandsDefinitions
l_nbRes = len(bands_definition.ListOfL2Resolution)
fre_list = []
stl_list = []
tgs_list = []
for r in range(0, l_nbRes):
l_res = bands_definition.ListOfL2Resolution[r]
l_l2bandcodes = bands_definition.get_list_of_l2_band_code(l_res)
l_l2bandidx = [
bands_definition.get_band_id_in_l2_coarse(b)
for b in l_l2bandcodes
]
l_l2bandchannels = ["Channel" + str(b + 1) for b in l_l2bandidx]
tdifl2_filename = os.path.join(slope_working,
"tdif_" + l_res + ".tif")
tdirl2_filename = os.path.join(slope_working,
"tdir_" + l_res + ".tif")
fre_image = os.path.join(slope_working, "fre_" + l_res + ".tif")
tgs_image = os.path.join(slope_working, "tgs_" + l_res + ".tif")
stl_image = os.path.join(slope_working, "stl_" + l_res + ".tif")
incangle_image = os.path.join(slope_working,
"incangle_" + l_res + ".tif")
# Extract tdif
tdifl2_app = extract_roi(interpolate_app.getoutput()["tdif"],
l_l2bandidx,
tdifl2_filename,
write_output=False)
self._l2_app_pipeline.add_otb_app(tdifl2_app)
tdifl2_image = tdifl2_app.getoutput()["out"]
# Extract tdir
tdirl2_app = extract_roi(interpolate_app.getoutput()["tdir"],
l_l2bandidx,
tdirl2_filename,
write_output=False)
self._l2_app_pipeline.add_otb_app(tdirl2_app)
tdirl2_image = tdirl2_app.getoutput()["out"]
# Compute incidence angles
param_incangle = {
"demasc":
dict_of_input.get("DEM").ASPList[r],
"demslc":
dict_of_input.get("DEM").SLPList[r],
"thetas":
float(
dict_of_input.get(
"L1Info").SolarAngle["sun_zenith_angle"]),
"phis":
float(
dict_of_input.get(
"L1Info").SolarAngle["sun_azimuth_angle"]),
"angles":
incangle_image
}
incangle_app = OtbAppHandler("IncidenceAngle",
param_incangle,
write_output=False)
self._l2_app_pipeline.add_otb_app(incangle_app)
# Compute slope correction
l_thetav = []
l_phiv = []
for ang in [
dict_of_input.get("L1Info").
ListOfViewingAnglesPerBandAtL2CoarseResolution[b]
for b in l_l2bandidx
]:
l_thetav.append(ang.get("incidence_zenith_angle"))
l_phiv.append(ang.get("incidence_azimuth_angle"))
param_slopecorr = {
"tdirsub":
tdirl2_image,
"tdifsub":
tdifl2_image,
"dtmasc":
dict_of_input.get("DEM").ASPList[r],
"dtmslc":
dict_of_input.get("DEM").SLPList[r],
"rhoenv":
dict_of_output["RhoEnv_" + l_res],
"incangle":
incangle_app.getoutput().get("angles"),
"sre":
dict_of_output["SRE_" + l_res],
"thetas":
float(
dict_of_input.get(
"L1Info").SolarAngle["sun_zenith_angle"]),
"phis":
float(
dict_of_input.get(
"L1Info").SolarAngle["sun_azimuth_angle"]),
"thetav":
l_thetav,
"phiv":
l_phiv,
"mincosi":
dict_of_input.get("L2COMM").get_value_f("SlopeMinCosI"),
"mincose":
dict_of_input.get("L2COMM").get_value_f("MinCosE"),
"mincosratio":
dict_of_input.get("L2COMM").get_value_f("MinCosRatio"),
"nodata":
dict_of_input.get("Params").get("RealL2NoData"),
"fre":
fre_image,
"tgs":
tgs_image,
"stl":
stl_image
}
slope_app = OtbAppHandler("SlopeCorrection",
param_slopecorr,
write_output=False)
self._l2_app_pipeline.add_otb_app(slope_app)
if is_croco_on("slopecorrection"):
dict_of_output["FRE_" + l_res] = fre_image
dict_of_output["TGS_" + l_res] = tgs_image
dict_of_output["STL_" + l_res] = stl_image
write_images([
slope_app.getoutput().get("fre"),
slope_app.getoutput().get("tgs"),
slope_app.getoutput().get("stl")
], [fre_image, tgs_image, stl_image])
else:
dict_of_output["FRE_" +
l_res] = slope_app.getoutput().get("fre")
dict_of_output["TGS_" +
l_res] = slope_app.getoutput().get("tgs")
dict_of_output["STL_" +
l_res] = slope_app.getoutput().get("stl")
fre_list.append(dict_of_output["FRE_" + l_res])
tgs_list.append(dict_of_output["TGS_" + l_res])
stl_list.append(dict_of_output["STL_" + l_res])
dict_of_output["FRE_List"] = fre_list
dict_of_output["TGS_List"] = tgs_list
dict_of_output["STL_List"] = stl_list
class MuscateL1ImageFileReaderBase(L1ImageReaderBase):
def __init__(self):
super(MuscateL1ImageFileReaderBase, self).__init__()
self._toa_scalar_list = []
self._toa_sub_list = []
self._l1toaimagelist = []
self._l2toaimagelist = []
self._sub_toa = ""
self._l2satmasklist = []
self._subsatimage = ""
self._l2zonemasklist = []
self._l2defectmasklist = []
self._l2dfpimagelist = []
self._l2edgmasklist = []
self._l2piximagelist = []
self._l2zoneimagelist = []
self._satmasksublist = []
self._zonemasksublist = []
self._nodatamasksublist = []
self._edgsubmask = ""
self._claimage = ""
self._vieimagelist = []
self._meanZenithMap = {}
self._meanAzimuthMap = {}
self._sol1image = ""
self._sol2image = ""
# Store the apps
self._toa_pipeline = OtbPipelineManager()
self._l2_dfp_pipeline = OtbPipelineManager()
self._l2_sat_pipeline = OtbPipelineManager()
self._l2_detf_pipeline = OtbPipelineManager()
self._l2_zone_pipeline = OtbPipelineManager()
self._l1toa_pipeline = OtbPipelineManager()
self._l2edg_pipeline = OtbPipelineManager()
self._satmasksub_pipeline = OtbPipelineManager()
self._satmask_pipeline = OtbPipelineManager()
self._l2pix_pipeline = OtbPipelineManager()
self._header_handler = None
def generate_cla_image(self, realL1Nodata, working_dir):
LOGGER.debug("MuscateL1ImageFileReader::GenerateCLAMask()")
# CLA image pipeline connection
# test if a CLA image file is available
cla_node = xml_tools.get_only_value(
self._header_handler.root,
"//Data_List/Data[Data_Properties/NATURE='Cloud_Altitude_Grid']/Data_File_List/DATA_FILE",
check=True)
if cla_node is not None:
self._claimage = self._header_handler.get_cla_image_filename()
else:
cla_filename = os.path.join(working_dir, "cla_constant_sub.tif")
cla_image = constant_image(self._dem.ALC,
realL1Nodata,
cla_filename,
write_output=True).getoutput()["out"]
self._claimage = cla_image
def generate_sol1_image(self, sol_h1, working_dir):
LOGGER.debug("MuscateL1ImageFileReader::GenerateSOL1Image()")
const_pi_18 = 0.01745329251994329577
# SOL1 image pipeline connection
# *********************************************************************************************************
sol_filename = os.path.join(working_dir, "sol1.tif")
mean_solar = self._header_handler.get_mean_solar_angles()
l_sol1 = sol_h1 * math.tan(const_pi_18 * mean_solar[0]) * math.sin(
const_pi_18 * mean_solar[1])
l_sol2 = sol_h1 * math.tan(const_pi_18 * mean_solar[0]) * math.cos(
const_pi_18 * mean_solar[1])
LOGGER.debug("SOL1 parameters : ")
LOGGER.debug(mean_solar)
LOGGER.debug(l_sol1)
LOGGER.debug(l_sol2)
cla_app_1 = constant_image(self._dem.ALC,
l_sol1,
sol_filename,
write_output=False)
cla_app_2 = constant_image(self._dem.ALC,
l_sol2,
sol_filename,
write_output=False)
out_concatenate = os.path.join(working_dir, "sol1_concat.tif")
param_concatenate = {
"il": [
cla_app_1.getoutput().get("out"),
cla_app_2.getoutput().get("out")
],
"out":
out_concatenate
}
OtbAppHandler("ConcatenateDoubleImages", param_concatenate)
self._sol1image = out_concatenate
def generate_sol2_image(self, sol_h2, working_dir):
raise MajaExceptionPluginMuscate(
"MuscateL1ImageFileReader::GenerateSOL2Image. Internal error: the GenerateSOL2Image() method must be define in the sub class"
)
def generate_vie_image(self, view_href, working_dir):
# *********************************************************************************************************
# VIE image pipeline connection
# *********************************************************************************************************
sol_filename = os.path.join(working_dir, "cla_constant_tmp.tif")
mean_view = self._header_handler.get_mean_viewing_angles()
l_vie1 = view_href * math.tan(mean_view[0]) * math.sin(mean_view[1])
l_vie2 = view_href * math.tan(mean_view[0]) * math.cos(mean_view[1])
vie_app_1 = constant_image(self._dem.ALC,
l_vie1,
sol_filename,
write_output=False)
vie_app_2 = constant_image(self._dem.ALC,
l_vie2,
sol_filename,
write_output=False)
out_concatenate = os.path.join(working_dir, "cla_constant.tif")
param_concatenate = {
"il": [
vie_app_1.getoutput().get("out"),
vie_app_2.getoutput().get("out")
],
"out":
out_concatenate
}
OtbAppHandler("ConcatenateDoubleImages", param_concatenate)
l_BandsDefinitions = self._plugin.BandsDefinitions
self._vieimagelist.append(out_concatenate)
def generate_toa(self, listOfTOAImageFileNames,
reflectanceMultiplicationValues, working):
"""
:param listOfTOAImageFileNames: ListOfStrings
:param reflectanceMultiplicationValues: ListOfDoubles
:return:
:rtype: string
"""
if len(reflectanceMultiplicationValues) != len(
listOfTOAImageFileNames):
raise MajaDataException(
"Not the same number of band images and coefficients : " +
str(len(reflectanceMultiplicationValues)) + " : " +
str(len(listOfTOAImageFileNames)))
# Init the projRef
l_ProjectionRef = ""
# =======> GENERATE TOA CACHING
# ---------------------------------------------------------------
# Get the number of band with the number of TOA image files set in the input product directory
l_NbBand = len(listOfTOAImageFileNames) # int
# Convert the input jp2 images in tif
for i in range(l_NbBand):
toaFilename = listOfTOAImageFileNames[i]
LOGGER.debug("Caching the <%s> image filename...", toaFilename)
LOGGER.debug("Reflectance quantification value: %s",
reflectanceMultiplicationValues[i])
# initialize the TOA reader
l_ImageFilename = os.path.join(working,
"toaconverted_" + str(i) + ".tif")
# extract_roi(toaFilename,[0],l_ImageFilename,working)
app = multiply_by_scalar(toaFilename,
reflectanceMultiplicationValues[i],
output_image=l_ImageFilename,
write_output=True)
self._toa_scalar_list.append(app.getoutput()["out"])
LOGGER.debug("Caching TOA images done ...")
return l_ProjectionRef
def generate_l2_toa_images(self, working_dir):
"""
:return:
"""
l_BandsDefinitions = self._plugin.BandsDefinitions
l_ListOfL2Resolution = l_BandsDefinitions.ListOfL2Resolution # ListOfStrings
l_NbL2Res = len(l_ListOfL2Resolution)
for l2res in range(l_NbL2Res):
# Current resolution: "R1" or "R2"
curRes = l_ListOfL2Resolution[l2res]
dtm = self._dem.ALTList[l2res]
# Get the list of band of the current resolution
listOfL2Bands = l_BandsDefinitions.get_list_of_l2_band_code(curRes)
nbBand = len(listOfL2Bands)
list_of_image = []
# For each band of the current resolution
for l_StrBandIdL2 in listOfL2Bands:
# Get the L1 band index associated to the L2 band code
l1BandIdx = l_BandsDefinitions.get_band_id_in_l1(l_StrBandIdL2)
# Generate the list of L2 TOA images per resolution
toa_l2_image = os.path.join(
working_dir, "aot_l2_{}.tif".format(l_StrBandIdL2))
app = resample(self._toa_scalar_list[l1BandIdx],
dtm,
toa_l2_image,
OtbResampleType.LINEAR_WITH_RADIUS,
write_output=False)
self._toa_pipeline.add_otb_app(app)
list_of_image.append(app.getoutput()["out"])
out_concatenate = os.path.join(
working_dir, "L2TOAImageListVector_" + curRes + ".tif")
param_concatenate = {"il": list_of_image, "out": out_concatenate}
concat_app = OtbAppHandler("ConcatenateDoubleImages",
param_concatenate,
write_output=False)
self._toa_pipeline.add_otb_app(concat_app)
self._l2toaimagelist.append(concat_app.getoutput().get("out"))
def generate_l1_toa_images(self, working_dir):
"""
:return:
"""
out_concatenate = os.path.join(working_dir, "L1TOAVector.tif")
param_concatenate = {
"il": self._toa_scalar_list,
"out": out_concatenate
}
app = OtbAppHandler("ConcatenateDoubleImages",
param_concatenate,
write_output=False)
self._l1toa_pipeline.add_otb_app(app)
self._l1toaimagelist.append(app.getoutput()["out"])
def generate_toa_sub_images(self, working):
dtm_coarse = self._dem.ALC
toa_sub_mini_pipe = OtbPipelineManager()
# For each band of the input product
for i, toa in enumerate(self._toa_scalar_list):
# undersampling at L2CoarseResolution
toa_sub_image = os.path.join(working, "aot_sub_{}.tif".format(i))
app = resample(toa,
dtm_coarse,
toa_sub_image,
OtbResampleType.LINEAR_WITH_RADIUS,
write_output=False)
self._toa_sub_list.append(app.getoutput()["out"])
toa_sub_mini_pipe.add_otb_app(app)
# end band loop
# *******************************************************************************************************
# TOA Sub image pipeline connection
# *******************************************************************************************************
toa_sub_image = os.path.join(working, "aot_sub.tif")
param_concatenate = {"il": self._toa_sub_list, "out": toa_sub_image}
OtbAppHandler("ConcatenateDoubleImages", param_concatenate)
self._sub_toa = toa_sub_image
toa_sub_mini_pipe.free_otb_app()
def generate_mask_rasters(self, p_ListOfTOABandCode, working):
# Generate mask rasters by rasterizing the gml mask per L2 resolution per band
LOGGER.info("Starting Muscate GenerateMaskRasters")
l_BandsDefinitions = self._plugin.BandsDefinitions
l2Area = None
l_ListOfL2Resolution = l_BandsDefinitions.ListOfL2Resolution # ListOfStrings
l_NbL2Res = len(l_ListOfL2Resolution)
l_ListOfL1Resolution = l_BandsDefinitions.ListOfL1Resolution # ListOfStrings
l_NbL1Res = len(l_ListOfL1Resolution)
dtm_coarse = self._dem.ALC
# Set a constant image if the gml masks are empty
tmp_constant_filename = os.path.join(working, "Masks_const.tif")
# initialize the L2 Elements
for l2res in range(l_NbL2Res):
self._l2defectmasklist.append([])
self._l2dfpimagelist.append(None)
# Init the coarse elements
for coarseband in p_ListOfTOABandCode:
self._l2satmasklist.append(None)
for l1res in range(l_NbL1Res):
self._satmasksublist.append(None)
# Test if the plugin has PIX and SAT ?
has_pix_masks = False
defectivPixFileNames = []
pix_node = xml_tools.get_only_value(
self._header_handler.root,
"//Mask_List/Mask/Mask_Properties/NATURE[.='Aberrant_Pixels']",
check=True)
if pix_node is not None:
has_pix_masks = True
defectivPixFileNames = self._header_handler.get_list_of_pix_mask_filenames(
)
has_sat_masks = False
satPixFileNames = []
sat_node = xml_tools.get_only_value(
self._header_handler.root,
"//Mask_List/Mask/Mask_Properties/NATURE[.='Saturation']",
check=True)
if sat_node is not None:
has_sat_masks = True
satPixFileNames = self._header_handler.get_list_of_l1_sat_image_filenames(
)
# Loop on L1Res
for l1res in range(l_NbL1Res):
# Current resolution: "R1" or "R2" or "R3"
curL1Res = l_ListOfL1Resolution[l1res]
# Create constant mask if it will be necessary at a time
tmp_constant_coarse_app = None
tmp_constant_L2_app = None
if not has_sat_masks or not has_pix_masks:
tmp_constant_coarse_filename = os.path.join(
working, "Masks_const_.tif")
tmp_constant_coarse_app = constant_image(
dtm_coarse,
0,
tmp_constant_coarse_filename,
write_output=False)
if curL1Res in l_ListOfL2Resolution:
tmp_constant_L2_filename = os.path.join(
working, "Masks_const_.tif")
tmp_constant_L2_app = constant_image(
self._dem.ALTList[l1res],
0,
tmp_constant_L2_filename + ":uint8",
write_output=False)
self._l2_sat_pipeline.add_otb_app(tmp_constant_L2_app)
# Get the list of band of the current resolution
listOfL1Bands = l_BandsDefinitions.get_list_of_l1_band_code(
curL1Res)
nbL1Bands = len(listOfL1Bands)
# For each band of the current resolution
l_l2zone_pipeline = OtbPipelineManager()
firstBandIdx = l_BandsDefinitions.get_band_id_in_l1(
listOfL1Bands[0])
if has_sat_masks:
l_strSatFileNameRef = satPixFileNames[firstBandIdx]
l_isSatSameFilesForBands = True
for l_BandIdxL1 in range(len(listOfL1Bands)):
# Get the L1 band index associated to the L2 band code
l_StrBandIdL1 = listOfL1Bands[l_BandIdxL1]
l1BandIdx = self._header_handler.get_index_of_band_code(
l_StrBandIdL1)
# Filenameverify
LOGGER.debug(l_StrBandIdL1 + " : " + str(l1BandIdx) +
" : " + satPixFileNames[l1BandIdx])
if l_strSatFileNameRef != satPixFileNames[l1BandIdx]:
l_isSatSameFilesForBands = False
LOGGER.debug("l_isSatSameFilesForBands = " +
str(l_isSatSameFilesForBands))
# SAT Mask generation
if l_isSatSameFilesForBands:
# Sat Masks generation
l_StrBandIdL1 = listOfL1Bands[0]
l1BandIdx = self._header_handler.get_index_of_band_code(
l_StrBandIdL1)
l_SATFFilename = satPixFileNames[l1BandIdx]
sat_mask = os.path.join(
working, "L1_SAT_Masks_{}.tif".format(curL1Res))
param_bintovec_dfp = {
"im": l_SATFFilename,
"out": sat_mask,
"nbcomp": nbL1Bands
}
sat_mask_app = OtbAppHandler("BinaryToVector",
param_bintovec_dfp,
write_output=False)
self._satmask_pipeline.add_otb_app(sat_mask_app)
if curL1Res in l_ListOfL2Resolution:
tmp_sat_l2_resample = os.path.join(
working, "tmp_sat_resample_{}.tif".format(l1res))
if l_BandsDefinitions.get_list_of_l1_band_code(curL1Res) != \
l_BandsDefinitions.get_list_of_l2_band_code(curL1Res):
tmp_l2_band_idx_list = []
#Create the list of index to build the current res sat mask
for l2band in l_BandsDefinitions.get_list_of_l2_band_code(
curL1Res):
l2BandIdxInHeader = self._header_handler.get_index_of_band_code(
l2band)
tmp_l2_band_idx_list.append(
self._header_handler.
get_l1_sat_image_index(l2BandIdxInHeader) -
1)
tmp_satmask_roi = os.path.join(
working,
"tmp_sat_extract_{}.tif".format(l1res))
tmp_extract_sat_toi_app = extract_roi(
sat_mask_app.getoutput()["out"],
tmp_l2_band_idx_list,
tmp_satmask_roi,
write_output=False)
self._satmask_pipeline.add_otb_app(
tmp_extract_sat_toi_app)
resamp_l2_app = resample(
tmp_extract_sat_toi_app.getoutput()["out"],
self._dem.ALTList[l1res],
tmp_sat_l2_resample,
OtbResampleType.LINEAR_WITH_RADIUS,
threshold=0.5,
padradius=4,
write_output=False)
self._satmask_pipeline.add_otb_app(resamp_l2_app)
self._l2satmasklist[l_ListOfL2Resolution.index(
curL1Res)] = resamp_l2_app.getoutput().get(
"out")
else:
resamp_l2_app = resample(
sat_mask_app.getoutput()["out"],
self._dem.ALTList[l1res],
tmp_sat_l2_resample + ":uint8",
OtbResampleType.LINEAR_WITH_RADIUS,
threshold=0.25,
padradius=4,
write_output=False)
self._satmask_pipeline.add_otb_app(resamp_l2_app)
self._l2satmasklist[l_ListOfL2Resolution.index(
curL1Res)] = resamp_l2_app.getoutput()["out"]
tmp_l1_band_idx_list = []
for l1band in listOfL1Bands:
l1BandIdxInHeader = self._header_handler.get_index_of_band_code(
l1band)
tmp_l1_band_idx_list.append(
self._header_handler.get_l1_sat_image_index(
l1BandIdxInHeader) - 1)
tmp_sat_roi = os.path.join(
working, "tmp_l1_extract_roi_sat_{}.tif".format(l1res))
tmp_sat_roi_app = extract_roi(
sat_mask_app.getoutput()["out"],
tmp_l1_band_idx_list,
tmp_sat_roi,
write_output=False)
l_sat_subthresholdvalue = self._GIPPL2COMMHandler.get_value_f(
"SaturationThresholdSub")
tmp_sat_resample = os.path.join(
working,
"tmp_extract_roi_sat_resample_{}.tif".format(l1res))
app_resample = resample(
tmp_sat_roi_app.getoutput().get("out"),
dtm_coarse,
tmp_sat_resample,
OtbResampleType.LINEAR_WITH_RADIUS,
threshold=l_sat_subthresholdvalue,
padradius=4,
write_output=True)
self._satmasksublist[l1res] = app_resample.getoutput(
)["out"]
else:
raise MajaExceptionPluginMuscate(
"Product format not supported : not the same file for band on SAT"
)
else:
# No sat available then put constants masks in outout
if curL1Res in l_ListOfL2Resolution:
self._l2satmasklist[l_ListOfL2Resolution.index(
curL1Res)] = tmp_constant_L2_app.getoutput().get("out")
for l1band in listOfL1Bands:
l1BandIdx = l_BandsDefinitions.get_band_id_in_l1(l1band)
self._satmasksublist[
l1BandIdx] = tmp_constant_coarse_app.getoutput().get(
"out")
# Verify if we can optimize the work if its the same file for all bands of
# the resolution and if the bands are in correct bit order
if has_pix_masks:
l_strDefectFileNameRef = defectivPixFileNames[firstBandIdx]
l_isDefectSameFilesForBands = True
for l_BandIdxL1 in range(len(listOfL1Bands)):
# Get the L1 band index associated to the L2 band code
l_StrBandIdL1 = listOfL1Bands[l_BandIdxL1]
l1BandIdx = self._header_handler.get_index_of_band_code(
l_StrBandIdL1)
# Filenameverify
if l_strDefectFileNameRef != defectivPixFileNames[
l1BandIdx]:
l_isDefectSameFilesForBands = False
LOGGER.debug("l_isDefectSameFilesForBands = " +
str(l_isDefectSameFilesForBands))
# Defective PIX (only in L2 resolution)
if l_isDefectSameFilesForBands:
if curL1Res in l_ListOfL2Resolution:
l_StrBandIdL1 = listOfL1Bands[0]
l1BandIdx = self._header_handler.get_index_of_band_code(
l_StrBandIdL1)
self._l2piximagelist.append(
defectivPixFileNames[l1BandIdx])
dfp_mask = os.path.join(
working, "L1_DFP_Masks_{}.tif".format(curL1Res))
param_bintovec_dfp = {
"im": defectivPixFileNames[l1BandIdx],
"out": dfp_mask,
"nbcomp": nbL1Bands
}
dfp_mask_app = OtbAppHandler("BinaryToVector",
param_bintovec_dfp,
write_output=False)
self._l2_dfp_pipeline.add_otb_app(dfp_mask_app)
tmp_dfp_l2_resample = os.path.join(
working, "tmp_dfp_resample_{}.tif".format(l1res))
resamp_l2_dfp_app = resample(
dfp_mask_app.getoutput().get("out"),
self._dem.ALTList[l1res],
tmp_dfp_l2_resample + ":uint8",
OtbResampleType.LINEAR_WITH_RADIUS,
threshold=0.25,
padradius=4,
write_output=False)
self._l2dfpimagelist[l_ListOfL2Resolution.index(
curL1Res)] = resamp_l2_dfp_app.getoutput().get(
"out")
self._l2_dfp_pipeline.add_otb_app(resamp_l2_dfp_app)
else:
raise MajaExceptionPluginMuscate(
"Product format not supported : not the same file for band on PIX"
)
else:
if curL1Res in l_ListOfL2Resolution:
self._l2dfpimagelist[l_ListOfL2Resolution.index(
curL1Res)] = tmp_constant_L2_app.getoutput().get("out")
self._l2piximagelist.append(
tmp_constant_L2_app.getoutput().get("out"))
# end res loop
def generate_sat_images(self, working_dir):
"""
:return:
"""
# *******************************************************************************************************
# IPSAT Sub image pipeline connection
# *******************************************************************************************************
out_concatenate = os.path.join(working_dir, "SubSatVector.tif")
param_concatenate = {
"il": self._satmasksublist,
"out": out_concatenate + ":uint8"
}
concat_app = OtbAppHandler("ConcatenateDoubleImages",
param_concatenate)
self._subsatimage = concat_app.getoutput().get("out")
def generate_edg_images(self, working_dir):
dtm_coarse = self._dem.ALC
# One band equal threshold
out_edg = os.path.join(working_dir, "tmp_EDG_oneBandEqual.tif")
param_edg = {
"im": self._l1toaimagelist[0],
"thresholdvalue": 0,
"equalvalue": 255,
"outsidevalue": 0,
"out": out_edg + ":uint8"
}
onebandequal_app = OtbAppHandler("OneBandEqualThreshold",
param_edg,
write_output=False)
# Resample to coarse
LOGGER.debug("Start IPEDGSub.")
tmp_edg_sub_resample = os.path.join(working_dir, "tmp_edg_sub.tif")
edg_sub_resample_app = resample(
onebandequal_app.getoutput().get("out"),
dtm_coarse,
tmp_edg_sub_resample,
OtbResampleType.LINEAR_WITH_RADIUS,
threshold=0.0,
padradius=4.0,
write_output=True)
# Threshold the output
#out_sub_edg = os.path.join(working_dir, "tmp_edg_sub_oneBandEqual.tif")
#param_sub_edg = {"im": edg_sub_resample_app.getoutput().get("out"),
# "thresholdvalue": 0,
# "equalvalue": 0,
# "outsidevalue": 1,
# "out": out_sub_edg + ":uint8"
# }
#onebandequal_sub_app = OtbAppHandler("OneBandEqualThreshold", param_sub_edg)
# Put in internal data
self._edgsubmask = edg_sub_resample_app.getoutput().get("out")
del onebandequal_app
del edg_sub_resample_app
LOGGER.debug("Start IPEDGSub done.")
# *******************************************************************************************************
# L2EDG pipeline connection
# *******************************************************************************************************
LOGGER.debug("Start L2EDG ...")
l_BandsDefinitions = self._plugin.BandsDefinitions
l_ListOfL2Resolution = l_BandsDefinitions.ListOfL2Resolution # ListOfStrings
# At L2 resolution
l_NbL2Res = len(l_ListOfL2Resolution)
# Set 1000 to edge pixels to identify the pixel contaminated by an edge pixel after resampling
#out_thresh = os.path.join(working_dir, "EDGThreshL2.tif")
#m_L2EDGThresholdApp = binary_threshold(self._edgsubmask,
# lower_threshold=0,
# inside_value=1000,
# outside_value=0,
# output_image=out_thresh + ":uint8",
# write_output=False)# //l_ThresholdImageFilter
#self._l2edg_pipeline.add_otb_app(m_L2EDGThresholdApp)
for r in range(l_NbL2Res):
res_str = l_ListOfL2Resolution[r]
# ExpandFilterPointer => PadAndResampleImageFilter => app ressampling
out_roi = os.path.join(working_dir,
"IPEDGRealL2_{}.tif".format(res_str))
roi_app = extract_roi(self._edgsubmask, [0],
out_roi,
write_output=False)
self._l2edg_pipeline.add_otb_app(roi_app)
out_ressampling = os.path.join(
working_dir, "IPEDGRealL2_{}.tif".format(res_str))
resamp_app = resample(roi_app.getoutput().get("out"),
self._dem.ALTList[r],
out_ressampling,
OtbResampleType.LINEAR,
threshold=0.001,
write_output=False)
self._l2edg_pipeline.add_otb_app(resamp_app)
# Set Threshold value to one because the expand filter interpolates values set to 0
# or 1000 in the first threshold and adds systematically CONST_EPSILON to the output value.
#m_L2EDGThresholdImage2_out = os.path.join(working_dir, "IPEDGMaskL2_{}.tif".format(res_str))
#m_L2EDGThresholdApp2 = binary_threshold(resamp_app.getoutput().get("out"),
# lower_threshold=1,
# inside_value=1,
# outside_value=0,
# output_image=m_L2EDGThresholdImage2_out + ":uint8",
# write_output=False)
self._l2edgmasklist.append(resamp_app.getoutput().get("out"))
#self._l2edg_pipeline.add_otb_app(m_L2EDGThresholdApp2)
def read(self, product_info, app_handler, l2comm, dem, pReadL1Mode):
"""product_info,plugin, l2comm,mode
:param product_info: L1ImageInformationsBase
:param pReadL1Mode: ReadL1ModeType
:return:
"""
LOGGER.info("Start Muscate L1 ImageFileReader ...")
product_filename = product_info.HeaderFilename
LOGGER.info("Start Muscate L1 ImageFileReader with filename : " +
product_filename)
working_dir = app_handler.get_directory_manager(
).get_temporary_directory("L1Read_", do_always_remove=True)
self._plugin.initialize(app_handler)
self._GIPPL2COMMHandler = l2comm
LOGGER.info("Start Muscate L1 ImageFileReader ...")
self._ReadL1Mode = pReadL1Mode
self._dem = dem
self._header_handler = product_info.HeaderHandler
# --------------------------------------
# Check the extension of th efile. For Muscate, must be .XML (in upper case)
IsValidSatellite = (self._header_handler is not None)
if not IsValidSatellite:
raise MajaExceptionPluginMuscate(
"The file <{}> is not a valid Muscate L1 product.".format(
product_filename))
# Get satellite name
self.m_Satellite = product_info.Satellite
# --------------------------------------
# Load the header tile file in the xml tile handler to read tile information
xmlTileFilename = product_info.xmlTileFilename
l_BandsDefinitions = self._plugin.BandsDefinitions # BandsDefinitions
# TOA & masks Reader connection
l_ListOfTOAImageFileNamesInHeader = self._header_handler.get_list_of_toa_image_filenames(
)
l_ListOfTOAImageFileNames = []
l_ListOfTOABandCode = l_BandsDefinitions.get_list_of_band_code_in_l2_coarse_sorted_by_index(
) # ListOfStrings
for band in l_ListOfTOABandCode:
LOGGER.debug(band)
l_ListOfTOAImageFileNames.append(l_ListOfTOAImageFileNamesInHeader[
self._header_handler.get_index_of_band_code(band)])
l_ListOfL1Resolution = l_BandsDefinitions.ListOfL1Resolution # ListOfStrings
l_ListOfL2Resolution = l_BandsDefinitions.ListOfL2Resolution # ListOfStrings
l_L1NoData = product_info.L1NoData
l_ReflectanceQuantificationValue = product_info.ReflectanceQuantification
l_RealL1NoData = l_L1NoData * l_ReflectanceQuantificationValue # RealNoDataType
# =======> GENERATE TOA
# Get calibration coeffs if activated
l_reflectanceMultiplicationValues = [] # ListOfDoubles
if xml_tools.as_bool(l2comm.get_value("CalAdjustOption")):
l_factors = xml_tools.as_float_list(
l2comm.get_value("CalAdjustFactor"))
if len(l_factors) != len(l_ListOfTOABandCode):
raise MajaDataException(
"Not the same number of Calibration coeffs than L1 bands ( {}, {} )"
.format(len(l_factors), len(l_ListOfTOABandCode)))
for i in range(len(l_ListOfTOABandCode)):
l_reflectanceMultiplicationValues.append(
l_ReflectanceQuantificationValue * l_factors[i])
else:
for i in range(len(l_ListOfTOABandCode)):
l_reflectanceMultiplicationValues.append(
l_ReflectanceQuantificationValue)
l_ProjectionRef = self._dem.ProjRef
self.generate_toa(l_ListOfTOAImageFileNames,
l_reflectanceMultiplicationValues,
working_dir) # string
if pReadL1Mode == ReadL1Mode.READ_L1_MODE_FOR_ALGORITHMS:
# --------------------------------------
# Get information of areas (footprint) of the product (Origin, Spacing and Size for L2 and L2Coarse resolution)
l_L2Dems = dem.ALTList
l_CoarseDem = dem.ALC
# =======> GENERATE TOA SUB IMAGES AT L2 COARSE RESOLUTION
LOGGER.debug("Start SubSampling ...")
self.generate_toa_sub_images(working_dir)
LOGGER.debug("Start SubSampling done.")
# the gml mask per L2 resolution per band
self.generate_mask_rasters(l_ListOfTOABandCode, working_dir)
# IPEDGSub and L2EDG pipeline connection
# L2 TOA image pipeline connection
# =======> GENERATE L2 TOA IMAGES
self.generate_l2_toa_images(working_dir)
# =======> GENERATE L1 TOA IMAGES
self.generate_l1_toa_images(working_dir)
# =======> GENERATE EDG IMAGES
self.generate_edg_images(working_dir)
# IPSAT Sub and L2SAT image pipeline connection
# =======> GENERATE SAT IMAGES
self.generate_sat_images(working_dir)
# CLA image pipeline connection
# *********************************************************************************************************
self.generate_cla_image(l_RealL1NoData, working_dir)
# SOL1 image pipeline connection
# *********************************************************************************************************
sol_h1 = self._plugin.ConfigUserCamera.get_Algorithms(
).get_GRID_Reference_Altitudes().get_SOLH1()
LOGGER.debug("sol_H1 : ")
LOGGER.debug(sol_h1)
self.generate_sol1_image(sol_h1, working_dir)
# SOL2 image pipeline connection
# *********************************************************************************************************
if self._plugin.GRIDReferenceAltitudesSOL2GridAvailable:
sol_h2 = self._plugin.ConfigUserCamera.get_Algorithms(
).get_GRID_Reference_Altitudes().get_SOLH2()
self.generate_sol2_image(sol_h2, working_dir)
# VIE image pipeline connection
# *********************************************************************************************************
vie_href = self._plugin.ConfigUserCamera.get_Algorithms(
).get_GRID_Reference_Altitudes().get_VIEHRef()
self.generate_vie_image(vie_href, working_dir)
# Fill the datas
self.dict_of_vals["IPEDGSubOutput"] = self._edgsubmask
self.dict_of_vals["SOL1Image"] = self._sol1image
self.dict_of_vals["SOL2Image"] = self._sol2image
if len(self._vieimagelist) > 2:
l_DTMBandCode = xml_tools.as_string_list(
l2comm.get_value("DTMViewingDirectionBandCode"))[0]
l_DTMBandIdx = self._plugin.BandsDefinitions.get_band_id_in_l2_coarse(
l_DTMBandCode)
LOGGER.info("DTMBandCode= " + l_DTMBandCode)
self.dict_of_vals["DTMVIEImage"] = self._vieimagelist[
l_DTMBandIdx]
else:
self.dict_of_vals["DTMVIEImage"] = self._vieimagelist[0]
self.dict_of_vals["IPTOASubOutput"] = self._sub_toa
self.dict_of_vals["L2TOAImageList"] = self._l2toaimagelist
self.dict_of_vals["ViewingZenithMeanMap"] = self._meanZenithMap
self.dict_of_vals["ViewingAzimuthMeanMap"] = self._meanAzimuthMap
self.dict_of_vals["CLAImage"] = self._claimage
self.dict_of_vals["IPSATSubOutput"] = self._subsatimage
if len(self._vieimagelist) > 2:
l_CLDBandCode = l2comm.get_value("CLDViewingDirectionBandCode")
l_CLDBandIdx = self._plugin.BandsDefinitions.get_band_id_in_l2_coarse(
l_CLDBandCode)
self.dict_of_vals["ShadowVIEImage"] = self._vieimagelist[
l_CLDBandIdx]
elif len(self._vieimagelist) > 1:
self.dict_of_vals["ShadowVIEImage"] = self._vieimagelist[1]
else:
self.dict_of_vals["ShadowVIEImage"] = self._vieimagelist[0]
if self._plugin.CirrusMasking:
l_CirrusBandCode = l2comm.get_value("CirrusBandCode")
l_CirrusBandIdx = self._plugin.BandsDefinitions.get_band_id_in_l2_coarse(
l_CirrusBandCode)
self.dict_of_vals["L1TOACirrusImage"] = self._toa_scalar_list[
l_CirrusBandIdx]
self.dict_of_vals["L2EDGOutputList"] = self._l2edgmasklist
self.dict_of_vals["L2SATImageList"] = self._l2satmasklist
self.dict_of_vals["L2PIXImageList"] = self._l2dfpimagelist
class Sentinel2L1ImageFileReaderBase(L1ImageReaderBase):
def __init__(self):
super(Sentinel2L1ImageFileReaderBase, self).__init__()
self._toa_scalar_list = []
self._toa_sub_list = []
self._l2toaimagelist = []
self._sol1image = ""
self._sub_toa = ""
self._l2satmasklist = []
self._l2satimagelist = []
self._subsatimage = ""
self._l2zonemasklist = []
self._l2defectmasklist = []
self._l2edgmasklist = []
self._l2piximagelist = []
self._l2dfpimagelist = []
self._l2zoneimagelist = []
self._satmasksublist = []
self._zonemasksublist = []
self._nodatamasksublist = []
self._edgsubmask = ""
self._claimage = ""
self._vieimagelist = []
self._meanZenithMap = {}
self._meanAzimuthMap = {}
# Store the apps
self._pipeline = OtbPipelineManager()
self._subedg_pipeline = OtbPipelineManager()
def get_l1_toa_cirrus_image(self):
"""
Get the L1 TOA reflectance image for Cirrus Band
:return:
"""
LOGGER.debug("Start L1 reading for L1 Cirrus band ...")
# TODO: GetCheckXMLFilesWithSchema TBD ConfigUserSystem.GetInstance()
l_BandsDefinitions = self._plugin.BandsDefinitions # BandsDefinitions
l_CirrusBandCode = self._GIPPL2COMMHandler.get_cirrus_band_code(
) # string
LOGGER.debug("Cirrus Viewing band code: %s", l_CirrusBandCode)
# ---------------------------------------------------------------
# get the index of the cirrus band in the list of the L1 bands
# curRes = "R2", bd = 1 <=> "B6" => i = 5
cirrusBandId = l_BandsDefinitions.L1BandMap.get(
l_CirrusBandCode, None) # int
LOGGER.debug("Cirrus BandId index: %s with nb index = %s %s )",
cirrusBandId, len(self._toa_scalar_list),
len(self._toa_scalar_list))
return self._toa_scalar_list[cirrusBandId]
def get_dtm_vie_image(self):
"""
Get DTMVIE Image
:return:
"""
l_DTMBandCode = self._GIPPL2COMMHandler.l2_comm_values.get(
"DTMViewingDirectionBandCode", None)[0]
LOGGER.debug("DTM Viewing band code: %s", l_DTMBandCode)
# TODO; check GetL2CoarseResolution grep L2CoarseResolution
l_DTMBandId = self._plugin.BandsDefinitions.get_band_id_in_l2_coarse(
l_DTMBandCode)
return self._vieimagelist[l_DTMBandId]
def get_shadow_vie_image(self):
"""
Get ShadowVIE Image
:return:
"""
l_CLDBandCode = self._GIPPL2COMMHandler.get_cld_viewing_direction_band_code(
)
l_CLDBandId = self._plugin.BandsDefinitions.get_band_id_in_l2_coarse(
l_CLDBandCode)
return self._vieimagelist[l_CLDBandId]
def generate_toa_caching(self, listOfTOAImageFileNames,
reflectanceMultiplicationValues, working):
"""
:param listOfTOAImageFileNames: ListOfStrings
:param reflectanceMultiplicationValues: ListOfDoubles
:return:
"""
if len(reflectanceMultiplicationValues) != len(
listOfTOAImageFileNames):
raise MajaDataException(
"Not the same number of band images and coefficients")
# ---------------------------------------------------------------
# Get the number of band with the number of TOA image files set in the input product directory
l_NbBand = len(listOfTOAImageFileNames) # int
# Convert the input jp2 images in tif
for i in range(l_NbBand):
toaFilename = listOfTOAImageFileNames[i]
LOGGER.debug("Caching the <%s> image filename...", toaFilename)
LOGGER.debug("Reflectance quantification value: %s",
reflectanceMultiplicationValues[i])
# Initialize the TOA reader
l_ImageFilename = os.path.join(working,
"toaconverted_" + str(i) + ".tif")
# extract_roi(toaFilename,[0],l_ImageFilename,working)
app = multiply_by_scalar(toaFilename,
reflectanceMultiplicationValues[i],
output_image=l_ImageFilename,
write_output=True)
self._pipeline.add_otb_app(app)
self._toa_scalar_list.append(app.getoutput()["out"])
def generate_toa_sub_images(self, working):
dtm_coarse = self._dem.ALC
# For each band of the input product
tmp_sub_toa_pipe = OtbPipelineManager()
for i, toa in enumerate(self._toa_scalar_list):
# undersampling at L2CoarseResolution
toa_sub_image = os.path.join(working, "aot_sub_{}.tif".format(i))
app = resample(toa,
dtm_coarse,
toa_sub_image,
OtbResampleType.LINEAR_WITH_RADIUS,
write_output=False)
self._toa_sub_list.append(app.getoutput()["out"])
tmp_sub_toa_pipe.add_otb_app(app)
# end band loop
# *******************************************************************************************************
# TOA Sub image pipeline connection
# *******************************************************************************************************
toa_sub_image = os.path.join(working, "aot_sub.tif")
param_concatenate = {"il": self._toa_sub_list, "out": toa_sub_image}
OtbAppHandler("ConcatenateDoubleImages", param_concatenate)
self._sub_toa = toa_sub_image
tmp_sub_toa_pipe.free_otb_app()
def rasterize_gml_masks(self, l1Resolution, l2res, l2Area, projectionRef,
l1Band, satFilename, defectFilename, zoneFilename,
boundingBox, gdalRasterizeMaskCmd,
gdalRasterizeDetCmd, tmp_constant_image, working):
"""
:param l1Resolution: string
:param l2res: int
:param l2Area: AreaType
:param projectionRef: string
:param l1Band: int
:param satFilename: string
:param defectFilename: string
:param zoneFilename: string
:param boundingBox: BoundingBoxType
:param gdalRasterizeMaskCmd: string
:param gdalRasterizeDetCmd: string
:return:
"""
# convert the band index in string
l1BandStr = str(l1Band)
LOGGER.debug(
"RasterizeGmlMasks mask for band: %s\n"
" - for l1Resolution = %s"
"\n - l2res = %s \n \n - l2Area = %s"
"\n - satFilename = %s \n - defectFilename = %s"
"\n - zoneFilename = %s \n", l1Band, l1Resolution, l2res,
l2Area, satFilename, defectFilename, zoneFilename)
# TODO: la generation d'une image constante cots.otb.otb_constant_image
# ------------------------------------------------------------
# -----------------------------------------------------------------------------------
# Rasterize gml masks at L2 resolution with gdal_rasterize system command
# -----------------------------------------------------------------------------------
# -----------------------------------------------------------------------------------
# Detectors FootPrint
# check if the gml mask contains features
count_zone = self.get_mask_feature_count(zoneFilename)
LOGGER.debug("MaskFeatureCount for " + zoneFilename + ": " +
str(count_zone))
if count_zone > 0:
# Get the shift between the detector index and the feature index
shift_res = self.get_detfoo_index_shift(zoneFilename)
if shift_res[0]:
sqlRequest = gdalRasterizeDetCmd
sqlRequest = sqlRequest + """-a fid2 -sql \'select fid + """ + \
str(shift_res[1]) + """ as fid2, * from MaskFeature \'"""
l_image_raster_filename = os.path.join(
working, "L2ZoneMaskReaders_Resol_" + l1Resolution +
"_band_id_" + l1BandStr + ".tif")
GdalRasterize().internal_rasterize_gml_macro(
zoneFilename, boundingBox.xmin, boundingBox.ymin,
boundingBox.xmax, boundingBox.ymax, l2Area.size[0],
l2Area.size[1], projectionRef, l_image_raster_filename,
sqlRequest)
self._l2zonemasklist[l2res].append(l_image_raster_filename)
else:
LOGGER.debug("Bad Order DETFOO detected")
sqlRequest = gdalRasterizeDetCmd
sqlRequest = sqlRequest + """-a fid2 -sql \'select fid + 1 as fid2, * from MaskFeature \'"""
l_image_raster_filename = os.path.join(
working, "L2ZoneMaskReaders_Resol_" + l1Resolution +
"_band_id_" + l1BandStr + ".tif")
GdalRasterize().internal_rasterize_gml_macro(
zoneFilename, boundingBox.xmin, boundingBox.ymin,
boundingBox.xmax, boundingBox.ymax, l2Area.size[0],
l2Area.size[1], projectionRef, l_image_raster_filename,
sqlRequest)
l_fid_dets = self.get_detfoo_index_detector_list(
zoneFilename, 1)
l_image_changed_filename = os.path.join(
working, "L2ZoneMaskReadersChanged_Resol_" + l1Resolution +
"_band_id_" + l1BandStr + ".tif")
change_values_param = {
"im": l_image_raster_filename,
"out": l_image_changed_filename,
"invals": [str(a) for a in l_fid_dets.keys()],
"outvals": [str(a) for a in l_fid_dets.values()]
}
change_values_app = OtbAppHandler("ChangeValues",
change_values_param)
self._l2zonemasklist[l2res].append(
change_values_app.getoutput().get("out"))
else:
self._l2zonemasklist[l2res].append(tmp_constant_image)
# -----------------------------------------------------------------------------------
# Saturated SAT
# check if the gml mask contains features
count_sat = self.get_mask_feature_count(satFilename)
LOGGER.debug("MaskFeatureCount for " + satFilename + ": " +
str(count_sat))
if count_sat > 0:
l_image_raster_filename = os.path.join(
working, "L2SatPixReaders_Resol_" + l1Resolution +
"_band_id_" + l1BandStr + ".tif")
GdalRasterize().internal_rasterize_gml_macro(
satFilename, boundingBox.xmin, boundingBox.ymin,
boundingBox.xmax, boundingBox.ymax, l2Area.size[0],
l2Area.size[1], projectionRef, l_image_raster_filename,
gdalRasterizeMaskCmd)
self._l2satimagelist[l2res].append(l_image_raster_filename)
else:
self._l2satimagelist[l2res].append(tmp_constant_image)
# -----------------------------------------------------------------------------------
# Defectiv PIX
# check if the gml mask contains features
count_pix = self.get_mask_feature_count(defectFilename)
LOGGER.debug("MaskFeatureCount for " + defectFilename + ": " +
str(count_pix))
if count_pix > 0:
l_image_raster_filename = os.path.join(
working, "L2DefectivPixReaders_Resol_" + l1Resolution +
"_band_id_" + l1BandStr + ".tif")
GdalRasterize().internal_rasterize_gml_macro(
defectFilename, boundingBox.xmin, boundingBox.ymin,
boundingBox.xmax, boundingBox.ymax, l2Area.size[0],
l2Area.size[1], projectionRef, l_image_raster_filename,
gdalRasterizeMaskCmd)
self._l2defectmasklist[l2res].append(l_image_raster_filename)
else:
self._l2defectmasklist[l2res].append(tmp_constant_image)
return count_zone, count_sat, count_pix
def generate_mask_rasters_gml(self, boundingBoxMap, l2Areas, projectionRef,
satPixFileNames, defectivPixFileNames,
zoneMaskFileNames, gdalRasterizeMaskCmd,
gdalRasterizeDetCmd, working):
"""
:param boundingBoxMap: BoundingBoxMapType
:param projectionRef: string
:param satPixFileNames: ListOfStrings
:param defectivPixFileNames: ListOfStrings
:param zoneMaskFileNames: ListOfStrings
:param gdalRasterizeMaskCmd: string
:param gdalRasterizeDetCmd: string
:param working: string
:return:
"""
# *******************************************************************************************************
# Generate mask rasters by rasterizing the gml mask per L2 resolution per band
# *******************************************************************************************************
LOGGER.debug("Start GML mask rasterization ...")
l_BandsDefinitions = self._plugin.BandsDefinitions
l2Area = None
l_ListOfL2Resolution = l_BandsDefinitions.ListOfL2Resolution # ListOfStrings
l_NbL2Res = len(l_ListOfL2Resolution)
for l2res in range(l_NbL2Res):
self._l2defectmasklist.append([])
self._l2zonemasklist.append([])
self._l2satimagelist.append([])
for l2res in range(l_NbL2Res):
# Current resolution: "R1" or "R2"
curRes = l_ListOfL2Resolution[l2res]
# Get the list of band of the current resolution
listOfL2Bands = l_BandsDefinitions.get_list_of_l2_band_code(curRes)
nbBand = len(listOfL2Bands)
l2Area = l2Areas[l2res]
counts = (0, 0, 0)
# Generate a constant image that will be used if the gml masks are empty (no feature)
tmp_constant_filename = os.path.join(
working, "const_{}.tif:uint8".format(l2res))
tmp_constant_image_app = constant_image(self._dem.ALTList[l2res],
0,
tmp_constant_filename,
write_output=True)
tmp_constant_image = tmp_constant_image_app.getoutput()["out"]
# For each band of the current resolution
for l_StrBandIdL2 in listOfL2Bands:
# Get the L1 band index associated to the L2 band code
l1BandIdx = l_BandsDefinitions.get_band_id_in_l1(l_StrBandIdL2)
LOGGER.debug(
"Sentinel2L1ImageFileReaderBase::GenerateMaskRasters: CurrentResol = %s, reading the "
"BandId L1 (associated) <%s> with index <%s>.", curRes,
l_StrBandIdL2, l1BandIdx)
# Read the bounding box for the resolution "l_L1Resolution"
l_BoundingBox = boundingBoxMap.get(curRes)
# =======> RASTERIZE GML MASKS
new_counts = self.rasterize_gml_masks(
curRes, l2res, l2Area, projectionRef, l1BandIdx,
satPixFileNames[l1BandIdx],
defectivPixFileNames[l1BandIdx],
zoneMaskFileNames[l1BandIdx], l_BoundingBox,
gdalRasterizeMaskCmd, gdalRasterizeDetCmd,
tmp_constant_image, working)
counts = tuple(map(operator.add, counts, new_counts))
# band loop
# *******************************************************************************************************
# L2 Zone mask pipeline connection
# *******************************************************************************************************
if counts[0] != 0:
zone_mask = os.path.join(working,
"Masks_Zone_{}.tif".format(l2res))
param_concatenate = {
"il": self._l2zonemasklist[l2res],
"out": zone_mask + ":uint8"
}
l2zoneimage_app = OtbAppHandler(
"ConcatenateDoubleImages",
param_concatenate,
write_output=(False
or is_croco_on("sentinel2.l1reader.l2zone")))
self._l2zoneimagelist.append(
l2zoneimage_app.getoutput().get("out"))
self._pipeline.add_otb_app(l2zoneimage_app)
else:
self._l2zoneimagelist.append(self._l2zonemasklist[l2res][0])
# *******************************************************************************************************
# PIX image pipeline connection (DEFECTIV PIX)
# *******************************************************************************************************
if counts[2] != 0:
# Compress (concatenate the vector image to binary image)
pix_vector_mask = os.path.join(
working, "Masks_Defect_Vector_{}.tif".format(l2res))
param_concatenate = {
"il": self._l2defectmasklist[l2res],
"out": pix_vector_mask + ":uint8"
}
pix_vector = OtbAppHandler("ConcatenateDoubleImages",
param_concatenate)
pix_mask = os.path.join(working,
"Masks_Defect_{}.tif".format(l2res))
param_binconcatenate = {
"im": pix_vector.getoutput().get("out"),
"out": pix_mask + ":uint16"
}
pix = OtbAppHandler(
"BinaryConcatenate",
param_binconcatenate,
write_output=(False
or is_croco_on("sentinel2.l1reader.l2pix")))
self._pipeline.add_otb_app(pix)
self._l2piximagelist.append(pix.getoutput().get("out"))
else:
self._l2piximagelist.append(self._l2defectmasklist[l2res][0])
# end res loop
LOGGER.debug("End GML mask rasterization ...")
def get_solar_grids(self, dtm, solarAnglesFile, solH1, working):
solar_grid_filename = os.path.join(working, "solar_grid.tif")
# angle_list_to_image()
solarangle_grid_app = angle_list_to_image(dtm,
solarAnglesFile,
solar_grid_filename,
write_output=False)
# Multiply by the solar reference altitude
solar_grid_mult_filename = os.path.join(working, "solar_grid_mult.tif")
param_scaled_solar = {
"im": solarangle_grid_app.getoutput().get("out"),
"coef": float(solH1),
"out": solar_grid_mult_filename
}
rta_scal_app = OtbAppHandler("MultiplyByScalar",
param_scaled_solar,
write_output=False)
# Expand at L2Coarse.
solar_grid_resamp_filename = os.path.join(working,
"solar_grid_resamp.tif")
resample(rta_scal_app.getoutput().get("out"), dtm,
solar_grid_resamp_filename, OtbResampleType.LINEAR)
self._sol1image = solar_grid_resamp_filename
def get_viewing_grids(self, band, l2CoarseArea, projectionRef, vieHRef,
satFilename, zoneFilename, nodataFilename,
listOfZone, boundingBox, viewing_angles,
gdalRasterizeMaskCmd, gdalRasterizeDetCmd,
gdalRasterizeNdtCmd, nbCoarseBands, working):
"""
:param band: int
:param l2CoarseArea: AreaType
:param projectionRef: string
:param vieHRef: double
:param satFilename: string
:param zoneFilename: string
:param nodataFilename: string
:param listOfZone: ListOfUIntegers
:param boundingBox: BoundingBoxType
:param viewing_angles:
:param gdalRasterizeMaskCmd: string
:param gdalRasterizeDetCmd: string
:param gdalRasterizeNdtCmd: string
:return:
"""
dtm_coarse = self._dem.ALC
# Set a constant image if the gml masks are empty
tmp_constant_filename = os.path.join(working, "Masks_sat_const.tif")
constant_image(dtm_coarse,
0,
tmp_constant_filename + ":uint8",
write_output=True)
# -----------------------------------------------------------------------------------
# Rasterize all the gml maks at L2 coarse resolution
# to generate the viewing grids
# -----------------------------------------------------------------------------------
# -----------------------------------------------------------------------------------
# Detectors FootPrint
# check if the gml mask contains features
count = self.get_mask_feature_count(zoneFilename)
if count > 0:
# Get the shift between the detector index and the feature index
shift_res = self.get_detfoo_index_shift(zoneFilename)
if shift_res[0]:
l_image_raster_filename = os.path.join(
working, "SubZoneMask_band_id_{}.tif".format(band))
# Additional parameters of the gdal_rasterize system command
sqlRequest = gdalRasterizeDetCmd + \
"-a fid2 -sql 'select fid + {} as fid2, * from MaskFeature'".format(shift_res[1])
GdalRasterize().internal_rasterize_gml_macro(
zoneFilename, boundingBox.xmin, boundingBox.ymin,
boundingBox.xmax, boundingBox.ymax, l2CoarseArea.size[0],
l2CoarseArea.size[1], projectionRef,
l_image_raster_filename, sqlRequest)
submask_resamp_filename = os.path.join(
working,
"SubZoneMask_resampled_band_id_{}.tif".format(band))
resample(l_image_raster_filename, dtm_coarse,
submask_resamp_filename, OtbResampleType.LINEAR)
self._zonemasksublist.append(submask_resamp_filename)
else:
LOGGER.debug("Bad Order DETFOO detected")
l_image_raster_filename = os.path.join(
working, "SubZoneMask_band_id_{}.tif".format(band))
# Additional parameters of the gdal_rasterize system command
sqlRequest = gdalRasterizeDetCmd + \
"-a fid2 -sql 'select fid + 1 as fid2, * from MaskFeature'"
GdalRasterize().internal_rasterize_gml_macro(
zoneFilename, boundingBox.xmin, boundingBox.ymin,
boundingBox.xmax, boundingBox.ymax, l2CoarseArea.size[0],
l2CoarseArea.size[1], projectionRef,
l_image_raster_filename, sqlRequest)
l_fid_dets = self.get_detfoo_index_detector_list(
zoneFilename, 1)
l_image_changed_filename = os.path.join(
working, "SubZoneMaskChanged_band_id_{}.tif".format(band))
change_values_param = {
"im": l_image_raster_filename,
"out": l_image_changed_filename + ":uint8",
"invals": [str(a) for a in l_fid_dets.keys()],
"outvals": [str(a) for a in l_fid_dets.values()]
}
change_values_app = OtbAppHandler("ChangeValues",
change_values_param)
submask_resamp_filename = os.path.join(
working,
"SubZoneMask_resampled_band_id_{}.tif".format(band))
resample(change_values_app.getoutput().get("out"), dtm_coarse,
submask_resamp_filename, OtbResampleType.LINEAR)
self._zonemasksublist.append(submask_resamp_filename)
else:
submask_resamp_filename = os.path.join(
working, "SubZoneMask_resampled_band_id_{}.tif".format(band))
resample(tmp_constant_filename, dtm_coarse,
submask_resamp_filename, OtbResampleType.LINEAR)
self._zonemasksublist.append(submask_resamp_filename)
# -----------------------------------------------------------------------------------
# Saturated pixel mask at L2 coarse
count = self.get_mask_feature_count(satFilename)
if count > 0:
l_image_raster_filename = os.path.join(
working, "SubSatMask_band_id_{}.tif".format(band))
GdalRasterize().internal_rasterize_gml_macro(
satFilename, boundingBox.xmin, boundingBox.ymin,
boundingBox.xmax, boundingBox.ymax, l2CoarseArea.size[0],
l2CoarseArea.size[1], projectionRef, l_image_raster_filename,
gdalRasterizeMaskCmd)
self._satmasksublist.append(l_image_raster_filename)
else:
self._satmasksublist.append(tmp_constant_filename)
# -----------------------------------------------------------------------------------
# No_data mask at L2 coarse
count = self.get_mask_feature_count(nodataFilename, "NODATA")
if count > 0:
l_image_raster_filename = os.path.join(
working, "SubNoDataMask_band_id_{}.tif".format(band))
GdalRasterize().internal_rasterize_gml_macro(
nodataFilename, boundingBox.xmin, boundingBox.ymin,
boundingBox.xmax, boundingBox.ymax, l2CoarseArea.size[0],
l2CoarseArea.size[1], projectionRef, l_image_raster_filename,
gdalRasterizeNdtCmd)
self._nodatamasksublist.append(l_image_raster_filename)
else:
self._nodatamasksublist.append(tmp_constant_filename)
l_VieAnglesGridList = []
l_nbDetectors = len(viewing_angles)
# Detector loop
LOGGER.debug("For each detectors (nb=%s) ...", l_nbDetectors)
for angle in viewing_angles:
l_vieAngleFile = angle.write(working)
# ---------------------------------------------------------------------------------
# Generate an image with the list of viewing angle values set in the header file
# ---------------------------------------------------------------------------------
viewing_grid_filename = os.path.join(
working,
"viewing_grid_{}_{}.tif".format(angle.detector_id, band))
# angle_list_to_image()
viewing_angle_app = angle_list_to_image(dtm_coarse,
l_vieAngleFile,
viewing_grid_filename,
write_output=False,
extrapolation=True)
# Expand at L2Coarse.
viewing_grid_resamp_filename = os.path.join(
working,
"viewing_grid_resamp_{}_{}.tif".format(angle.detector_id,
band))
resample(viewing_angle_app.getoutput().get("out"), dtm_coarse,
viewing_grid_resamp_filename, OtbResampleType.LINEAR)
# add images in a list
l_VieAnglesGridList.append(viewing_grid_resamp_filename)
# end detector loop
LOGGER.debug(
"Start ConcatenatePerZoneVectorImageFilter for band id [%s]...",
band)
# -----------------------------------------------------------------------------------
# Generate the angle images using the zone (detector) mask
# -----------------------------------------------------------------------------------
# Concatenate all the detectors
viewing_concat_filename = os.path.join(
working, "viewing_concat_{}.tif".format(band))
param_concat_perzone = {
"mask": self._zonemasksublist[-1],
"il": l_VieAnglesGridList,
"zonelist": listOfZone,
"out": viewing_concat_filename
}
concat_perzone = OtbAppHandler("ConcatenatePerZone",
param_concat_perzone)
# Multiply by reference altitude
viewing_grid_mult_filename = os.path.join(
working, "viewing_grid_mult_{}.tif".format(band))
param_scaled_solar = {
"im": viewing_concat_filename,
"coef": float(vieHRef),
"out": viewing_grid_mult_filename
}
view_scale_app = OtbAppHandler("MultiplyByScalar",
param_scaled_solar,
write_output=True)
self._vieimagelist.append(view_scale_app.getoutput().get("out"))
l_nbZones = len(listOfZone)
LOGGER.debug("Start Loop for Zone (nb=" + str(l_nbZones) + ")...")
for d in range(l_nbZones):
l_zone = listOfZone[d]
# -----------------------------------------------------------------------------------
# Compute average values of zenithal and azimuthal angles grid per zone (detector in level1B)
# -----------------------------------------------------------------------------------
# VAP Reader connection (from ATB)
tmp_azi = os.path.join(working,
"tmp_azi_{}_{}.tif".format(band, l_zone))
tmp_azi_image = extract_roi(l_VieAnglesGridList[d], [1], tmp_azi)
param_stats = {
"im": tmp_azi,
"exclude": 1,
"mask": self._zonemasksublist[band],
"maskforeground": int(l_zone)
}
l2_stat = OtbAppHandler("Stats", param_stats)
azi_mean = l2_stat.getoutput().get("mean")
tmp_zen = os.path.join(working,
"tmp_zen_{}_{}.tif".format(band, l_zone))
tmp_zen_image = extract_roi(l_VieAnglesGridList[d], [0], tmp_zen)
param_stats = {
"im": tmp_zen,
"exclude": 1,
"mask": self._zonemasksublist[band],
"maskforeground": int(l_zone)
}
l2_stat = OtbAppHandler("Stats", param_stats)
zen_mean = l2_stat.getoutput().get("mean")
tmp_mean = (zen_mean, azi_mean)
l_ViewingAngleMean = grid_to_angle(tmp_mean)
l_ViewingAngleMeanDeg = (l_ViewingAngleMean[0] * 180.0 / math.pi,
l_ViewingAngleMean[1] * 180.0 / math.pi)
# Add a vector to mean maps
if l_zone not in self._meanZenithMap:
self._meanZenithMap[listOfZone[d]] = ["0"] * nbCoarseBands
if l_zone not in self._meanAzimuthMap:
self._meanAzimuthMap[listOfZone[d]] = ["0"] * nbCoarseBands
self._meanZenithMap[listOfZone[d]][band] = str(
l_ViewingAngleMeanDeg[0])
self._meanAzimuthMap[listOfZone[d]][band] = str(
l_ViewingAngleMeanDeg[1])
LOGGER.debug(" For BandId[" + str(band) + "], zone [" +
str(listOfZone[d]) + "] . Mean 'GRID View angles'=" +
str(tmp_mean) + " . Mean 'View angles'=" +
str(l_ViewingAngleMean[0]) + ":" +
str(l_ViewingAngleMean[1]) + " rad. ou " +
str(l_ViewingAngleMeanDeg[0]) + ":" +
str(l_ViewingAngleMeanDeg[1]) + " deg.")
LOGGER.debug("Start Loop for Zone done.")
def generate_edg_images(self, working_dir):
"""
:param working_dir: String
:return:
"""
dtm_coarse = self._dem.ALC
list_of_binary_threshold = []
m_OrFilter1List = []
m_OrFilter2List = []
# *******************************************************************************************************
# IPEDGSub pipeline connection
# *******************************************************************************************************
LOGGER.debug("Start IPEDGSub ...")
# At L2 coarse resolution
# Set to 0 the pixel that are not edges in the zone (detector) mask image raster
out_thresh_0 = os.path.join(working_dir, "IPEDGOrFilter1SubMask_0.tif")
out_binary_threshold_ipedg_image_b1 = binary_threshold(
input_image=self._zonemasksublist[0],
inside_value=0,
outside_value=1,
output_image=out_thresh_0 + ":uint8",
lower_threshold=1,
write_output=False)
# Concatenate the zone image and the nodata one
m_OrFilter2List.append(
out_binary_threshold_ipedg_image_b1.getoutput()["out"])
m_OrFilter2List.append(self._nodatamasksublist[0])
l_NbBand = len(self._zonemasksublist)
# band loop
for l_band in range(l_NbBand - 1):
out_thresh_1 = os.path.join(
working_dir,
"IPEDGThreshFilter1SubMask_{}.tif".format(l_band + 1))
out_binary_threshold_ipedg_image = binary_threshold(
input_image=self._zonemasksublist[l_band + 1],
lower_threshold=1,
inside_value=0,
outside_value=1,
output_image=out_thresh_1 + ":uint8",
write_output=False)
self._subedg_pipeline.add_otb_app(out_binary_threshold_ipedg_image)
m_OrFilter2List.append(
out_binary_threshold_ipedg_image.getoutput().get("out"))
m_OrFilter2List.append(self._nodatamasksublist[l_band + 1])
#OR filter
out_edg = os.path.join(working_dir, "IPEDGubMask.tif")
or_edg_sub_app = band_math_or(m_OrFilter2List,
out_edg + ":uint8",
write_output=True)
# Allocation of 7 Go in S2 MUSCATE case
self._edgsubmask = or_edg_sub_app.getoutput().get("out")
self._subedg_pipeline.free_otb_app()
LOGGER.debug("Start IPEDGSub done.")
# *******************************************************************************************************
# L2EDG pipeline connection
# *******************************************************************************************************
LOGGER.debug("Start L2EDG ...")
l_BandsDefinitions = self._plugin.BandsDefinitions
l_ListOfL2Resolution = l_BandsDefinitions.ListOfL2Resolution # ListOfStrings
# At L2 resolution
l_NbL2Res = len(l_ListOfL2Resolution)
for r in range(l_NbL2Res):
res_str = l_ListOfL2Resolution[r]
# Set 1000 to edge pixels to identify the pixel contaminated by an edge pixel after resampling
out_thresh = os.path.join(working_dir,
"EDGThreshL2_{}.tif".format(res_str))
#m_L2EDGThreshold_app = binary_threshold(self._edgsubmask,
# lower_threshold=0,
# inside_value=0,
# outside_value=1000,
# output_image=out_thresh + ":uint8",
# write_output=False)
#self._pipeline.add_otb_app(m_L2EDGThreshold_app)
# ExpandFilterPointer => PadAndResampleImageFilter => app ressampling
out_ressampling = os.path.join(
working_dir, "IPEDGRealL2_{}.tif".format(res_str))
resamp_app = resample(self._edgsubmask,
self._dem.ALTList[r],
out_ressampling + ":uint8",
OtbResampleType.LINEAR,
threshold=0.000001,
write_output=False)
self._pipeline.add_otb_app(resamp_app)
# Set Threshold value to one because the expand filter interpolates values set to 0
# or 1000 in the first threshold and adds systematically CONST_EPSILON to the output value.
#m_L2EDGThresholdImage2_out = os.path.join(working_dir, "IPEDGMaskL2_{}.tif".format(res_str))
#m_L2EDGThresholdImageApp2 = binary_threshold(
# resamp_app.getoutput().get("out"),
# lower_threshold=0.,
# inside_value=0,
# outside_value=1,
# output_image=m_L2EDGThresholdImage2_out + ":uint8",
# write_output=False)
#self._pipeline.add_otb_app(m_L2EDGThresholdImageApp2)
self._l2edgmasklist.append(resamp_app.getoutput().get("out"))
def generate_l2_toa_images(self, working_dir):
"""
:param working_dir:
:return:
"""
l_BandsDefinitions = self._plugin.BandsDefinitions
l_ListOfL2Resolution = l_BandsDefinitions.ListOfL2Resolution # ListOfStrings
l_NbL2Res = len(l_ListOfL2Resolution)
for l2res in range(l_NbL2Res):
# Current resolution: "R1" or "R2"
curRes = l_ListOfL2Resolution[l2res]
# Get the list of band of the current resolution
listOfL2Bands = l_BandsDefinitions.get_list_of_l2_band_code(curRes)
nbBand = len(listOfL2Bands)
list_of_image = []
# For each band of the current resolution
for l_StrBandIdL2 in listOfL2Bands:
# Get the L1 band index associated to the L2 band code
l1BandIdx = l_BandsDefinitions.get_band_id_in_l1(l_StrBandIdL2)
# Generate the list of L2 TOA images per resolution
list_of_image.append(self._toa_scalar_list[l1BandIdx])
out_concatenate = os.path.join(
working_dir, "L2TOAImageListVector_" + curRes + ".tif")
param_concatenate = {"il": list_of_image, "out": out_concatenate}
l2toa_concat_app = OtbAppHandler(
"ConcatenateDoubleImages",
param_concatenate,
write_output=(False
or is_croco_on("sentinel2.l1reader.l2toa")))
self._pipeline.add_otb_app(l2toa_concat_app)
self._l2toaimagelist.append(
l2toa_concat_app.getoutput().get("out"))
def generate_sat_images(self, working_dir):
"""
:return:
"""
# *******************************************************************************************************
# IPSAT Sub image pipeline connection
# *******************************************************************************************************
out_concatenate = os.path.join(working_dir, "SubSatVector.tif")
param_concatenate = {
"il": self._satmasksublist,
"out": out_concatenate + ":uint8"
}
concat_app = OtbAppHandler("ConcatenateDoubleImages",
param_concatenate)
self._subsatimage = concat_app.getoutput().get("out")
# *******************************************************************************************************
# L2SAT image pipeline connection
# *******************************************************************************************************
l_BandsDefinitions = self._plugin.BandsDefinitions
l_ListOfL2Resolution = l_BandsDefinitions.ListOfL2Resolution
# At L2 resolution
l_NbL2Res = len(l_ListOfL2Resolution)
for i in range(l_NbL2Res):
str_res = l_ListOfL2Resolution[i]
out_concatenate = os.path.join(
working_dir, "L2SATVector_{}.tif".format(str_res))
param_concatenate = {
"il": self._l2satimagelist[i],
"out": out_concatenate + ":uint8"
}
sat_image = OtbAppHandler("ConcatenateDoubleImages",
param_concatenate,
write_output=False)
self._pipeline.add_otb_app(sat_image)
self._l2satmasklist.append(sat_image.getoutput().get("out"))
def generate_cla_images(self, realL1Nodata, working):
cla_filename = os.path.join(working, "cla_constant_sub.tif")
cla_image = constant_image(self._dem.ALC,
realL1Nodata,
cla_filename,
write_output=True).getoutput()["out"]
self._claimage = cla_image
@staticmethod
def get_mask_feature_count(maskFilename, regex=None):
"""
Get the number of layers in a gml file
:param maskFilename:
:return:
"""
maskHandler = get_root_xml(maskFilename, deannotate=True)
xnodes = get_all_values(maskHandler,
"//*[local-name(.) = 'MaskFeature']")
if xnodes is not None:
if regex is None:
return len(xnodes)
else:
nb = 0
for x in xnodes:
for a in list(x.items()):
if regex in a[1]:
nb = nb + 1
return nb
else:
return 0
@staticmethod
def get_detfoo_index_shift(maskFilename):
"""
Get the shift between the detector index and the feature index in the MSK_DETFOO mask
:param maskFilename: string
:return:
"""
shift = 0
# Load the MSK_DETFOO gml file
maskHandler = get_root_xml(maskFilename, deannotate=True)
# Get the mask feature nodes
# as : <eop:MaskFeature gml:id="detector_footprint-B02-10-2">
xnodes = get_all_values(maskHandler,
"//*[local-name(.) = 'MaskFeature']")
# Two indices : the detector index and the feature index
detId = 0
# For each feature
for i in range(len(xnodes)):
feat = xnodes[i]
prevShift = shift
prevDetId = detId
# "detector_footprint-B02-10-2"
attribute = list(feat.items())[0][1]
element = attribute.split('-')
# Get the detector index and the feature index
detId = int(element[-2])
featId = int(element[-1])
# The shift between those two indices must be constant
# because it should not have a gap of detector between to consecutive features
shift = detId - featId
# Check the index values of the next features
if (i > 0) and ((shift != prevShift) or (detId != prevDetId + 1)):
LOGGER.warn(
"Sentinel2L1ImageFileReaderBase::get_detfoo_index_shift: The detector indices are not in ascending order "
"or do not have a constant shift with the feature indices in the MSK_DETFOO "
+ maskFilename + " !")
return False, shift
return True, shift
# Get the shift between the detector index and the feature index in the MSK_DETFOO mask
@staticmethod
def get_detfoo_index_detector_list(maskFilename, shift):
l_result = {}
# Load the MSK_DETFOO gml file
maskHandler = get_root_xml(maskFilename, deannotate=True)
# Get the mask feature nodes
# as : <eop:MaskFeature gml:id="detector_footprint-B02-10-2">
xnodes = get_all_values(maskHandler,
"//*[local-name(.) = 'MaskFeature']")
# Two indices : the detector index and the feature index
detId = 0
# For each feature
for i in range(len(xnodes)):
feat = xnodes[i]
# Get the attribute of the feature node
# "detector_footprint-B02-10-2"
attribute = list(feat.items())[0][1]
element = attribute.split('-')
# Get the detector index and the feature index
detId = int(element[-2])
featId = int(element[-1])
l_result[featId + shift] = detId
return l_result
class L2ImageReaderBase(object):
def __init__(self):
self.Date = None
self.Satellite = ""
self.Acquisition_Date = ""
self.Validity_Start = ""
self.Validity_Stop = ""
self.Nick_Name = ""
self.Reference_SiteDefinition_Id = ""
self.Adjacency_Effects_And_Slope_Correction = False
self.Reflectance_Quantification_Value = ""
self.Nodata_Value = None
self.RealL2Nodata_Value = None
self.VAP_Nodata_Value = None
self.RealVAP_Nodata_Value = None
self.VAP_Quantification_Value = None
self.AOT_Nodata_Value = None
self.RealAOT_Nodata_Value = None
self.AOT_Quantification_Value = None
self.ProjectionRef = ""
self.List_of_GIPP_Files = []
self.PluginName = ""
self.FileClass = ""
self.LevelType = ""
self.Prefix = ""
self.ListOfBandTheoreticalWavelengthOfTheReflectancesComposite = []
self.ListOfBandTheoreticalWavelengthOfTheLTCComposite = []
self.STOBandTheoreticalWavelengthIsPresent = False
self.STOBandTheoreticalWavelengthValue = 0
self.GlobalProductFilename = None
self.ProductFilename = ""
self.STOListOfStringDates = []
self.L2PrivateImageFilenamesProvider = None
self.dict_of_vals = {}
self._coarse_pipeline = OtbPipelineManager()
self._dem = None
def read_info(self, product_filename, enable_public_data):
raise MajaNotYetImplementedException(
"Could not instanciate base class")
def can_read(self, product_filename):
raise MajaNotYetImplementedException(
"Could not instanciate base class")
def read(self, product_filename, app_handler, enable_public_data):
raise MajaNotYetImplementedException(
"Could not instanciate base class")
def detect_l2_products(self, dir, product_list):
raise MajaNotYetImplementedException(
"Could not instanciate base class")
def get_value(self, key):
if key in self.dict_of_vals:
return self.dict_of_vals.get(key)
else:
raise MajaPluginBaseException("No " + key +
" available in the l2 reader.")
def read_public_images(self, p_L2ImagesFilenamesProvider,
p_ReflectanceQuantificationValue, p_PluginBase,
working_dir):
raise MajaNotYetImplementedException(
"Could not instanciate base class")
def read_private_images(self, p_L2PrivateImageFilenamesProvider,
p_ReflectanceQuantificationValue, p_PluginBase,
working_dir):
l_NumberOfComponentsPerPixelForPWAAndTWA = len(
p_PluginBase.BandsDefinitions.L2CoarseBandMap)
# *************************************************************************************************************
# Register the Header for the L2 Input image file
# *************************************************************************************************************
l_ReflectanceQuantificationValue = p_ReflectanceQuantificationValue
# Read the list of wavelength of the RTA/RTC and RCR list of bands in the HDR xml header file
l_RTAHeaderFilename = p_L2PrivateImageFilenamesProvider.get_rta_header_filename(
)
l_RTAHandler = HeaderImageEarthExplorerXMLFileHandler(
l_RTAHeaderFilename)
self.ListOfBandTheoreticalWavelengthOfTheReflectancesComposite = l_RTAHandler.get_list_of_band_theoretical_wavelength(
)
# ********************************************************************************************************/
# #* RTA Reader connection */
LOGGER.debug(
"L2ImageFileReaderBase::ReadPrivateImages - RTA image filename: " +
p_L2PrivateImageFilenamesProvider.get_rta_image_filename() + ".")
tmp_rta_resample = os.path.join(working_dir, "tmp_rta_resample.tif")
app_rta_resample = resample(
p_L2PrivateImageFilenamesProvider.get_rta_image_filename(),
self._dem.ALC,
tmp_rta_resample,
OtbResampleType.BCO,
padradius=4.0,
write_output=False)
tmp_rta_scale = os.path.join(working_dir, "tmp_rta_scale.tif")
param_scaled_rta = {
"im": app_rta_resample.getoutput().get("out"),
"coef": l_ReflectanceQuantificationValue,
"out": tmp_rta_scale
}
rta_scal_app = OtbAppHandler("MultiplyByScalar",
param_scaled_rta,
write_output=True)
self._coarse_pipeline.add_otb_app(rta_scal_app)
self._coarse_pipeline.add_otb_app(app_rta_resample)
self.dict_of_vals["RTAImage"] = rta_scal_app.getoutput()["out"]
# ********************************************************************************************************/
# * RTC Reader connection */
# ********************************************************************************************************/
LOGGER.debug(
"L2ImageFileReaderBase::ReadPrivateImages - RTC image filename: " +
p_L2PrivateImageFilenamesProvider.get_rtc_image_filename() + ".")
tmp_rtc_resample = os.path.join(working_dir, "tmp_rtc_resample.tif")
app_rtc_resample = resample(
p_L2PrivateImageFilenamesProvider.get_rtc_image_filename(),
self._dem.ALC,
tmp_rtc_resample,
OtbResampleType.BCO,
padradius=4.0,
write_output=False)
tmp_rtc_scale = os.path.join(working_dir, "tmp_rtc_scale.tif")
param_scaled_rtc = {
"im": app_rtc_resample.getoutput().get("out"),
"coef": l_ReflectanceQuantificationValue,
"out": tmp_rtc_scale
}
rtc_scal_app = OtbAppHandler("MultiplyByScalar",
param_scaled_rtc,
write_output=True)
self._coarse_pipeline.add_otb_app(rtc_scal_app)
self._coarse_pipeline.add_otb_app(app_rtc_resample)
self.dict_of_vals["RTCImage"] = rtc_scal_app.getoutput()["out"]
# ********************************************************************************************************/
# * RCR Reader connection */
# ********************************************************************************************************/
LOGGER.debug(
"L2ImageFileReaderBase::ReadPrivateImages - RCR image filename: " +
p_L2PrivateImageFilenamesProvider.get_rcr_image_filename() + ".")
tmp_rcr_resample = os.path.join(working_dir, "tmp_rcr_resample.tif")
app_rcr_resample = resample(
p_L2PrivateImageFilenamesProvider.get_rcr_image_filename(),
self._dem.ALC,
tmp_rcr_resample,
OtbResampleType.BCO,
padradius=4.0,
write_output=False)
tmp_rcr_scale = os.path.join(working_dir, "tmp_rcr_scale.tif")
param_scaled_rcr = {
"im": app_rcr_resample.getoutput().get("out"),
"coef": l_ReflectanceQuantificationValue,
"out": tmp_rcr_scale
}
rcr_scal_app = OtbAppHandler("MultiplyByScalar",
param_scaled_rcr,
write_output=True)
self._coarse_pipeline.add_otb_app(rcr_scal_app)
self._coarse_pipeline.add_otb_app(app_rcr_resample)
self.dict_of_vals["RCRImage"] = rcr_scal_app.getoutput()["out"]
# ********************************************************************************************************/
# * PXD Reader connection */
# ********************************************************************************************************/
LOGGER.debug(
"L2ImageFileReaderBase::ReadPrivateImages - PXD image filename: " +
p_L2PrivateImageFilenamesProvider.get_pxd_image_filename() + ".")
tmp_pxd_resample = os.path.join(working_dir, "tmp_pxd_resample.tif")
app_pxd_resample = resample(
p_L2PrivateImageFilenamesProvider.get_pxd_image_filename(),
self._dem.ALC,
tmp_pxd_resample,
OtbResampleType.BCO,
padradius=4.0,
write_output=True)
self._coarse_pipeline.add_otb_app(app_pxd_resample)
self.dict_of_vals["PXDImage"] = app_pxd_resample.getoutput().get("out")
# ********************************************************************************************************/
# * WAS Reader connection (WAM product) */
# ********************************************************************************************************/
tmp_was = os.path.join(working_dir, "tmp_was.tif")
was_extr_app = extract_roi(
p_L2PrivateImageFilenamesProvider.get_wam_image_filename(), [0],
tmp_was,
write_output=False)
tmp_was_resample = os.path.join(working_dir, "tmp_was_resample.tif")
app_was_resample = resample(was_extr_app.getoutput().get("out"),
self._dem.ALC,
tmp_was_resample,
OtbResampleType.BCO,
padradius=4.0,
write_output=True)
self._coarse_pipeline.add_otb_app(was_extr_app)
self._coarse_pipeline.add_otb_app(app_was_resample)
self.dict_of_vals["WASImage"] = app_was_resample.getoutput()["out"]
# ********************************************************************************************************/
# * PWA Reader connection (WAM product) */
# ********************************************************************************************************/
tmp_pwa = os.path.join(working_dir, "tmp_pwa.tif")
pwa_image_app = extract_roi(
p_L2PrivateImageFilenamesProvider.get_wam_image_filename(), [1],
tmp_pwa + ":uint8",
write_output=False)
self._coarse_pipeline.add_otb_app(pwa_image_app)
tmp_pwa_resample = os.path.join(working_dir, "tmp_pwa_resample.tif")
app_pwa_resample = resample(pwa_image_app.getoutput().get("out"),
self._dem.ALC,
tmp_pwa_resample,
OtbResampleType.BCO,
padradius=4.0,
write_output=False)
self._coarse_pipeline.add_otb_app(app_pwa_resample)
tmp_vecpwa = os.path.join(working_dir, "tmp_pwa_vec.tif")
param_binpwa = {
"im": app_pwa_resample.getoutput()["out"],
"out": tmp_vecpwa,
"nbcomp": l_NumberOfComponentsPerPixelForPWAAndTWA
}
binpwa_app = OtbAppHandler("BinaryToVector",
param_binpwa,
write_output=True)
self._coarse_pipeline.add_otb_app(binpwa_app)
self.dict_of_vals["PWAImage"] = binpwa_app.getoutput()["out"]
# ********************************************************************************************************/
# * TWA Reader connection (WAM product) */
# ********************************************************************************************************/
tmp_twa = os.path.join(working_dir, "tmp_twa.tif")
twa_image = extract_roi(
p_L2PrivateImageFilenamesProvider.get_wam_image_filename(), [2],
tmp_twa + ":uint8",
write_output=False)
tmp_twa_resample = os.path.join(working_dir, "tmp_twa_resample.tif")
app_twa_resample = resample(twa_image.getoutput().get("out"),
self._dem.ALC,
tmp_twa_resample,
OtbResampleType.BCO,
padradius=4.0,
write_output=False)
self._coarse_pipeline.add_otb_app(app_twa_resample)
tmp_vectwa = os.path.join(working_dir, "tmp_twa_vec.tif")
param_bintwa = {
"im": app_twa_resample.getoutput().get("out"),
"out": tmp_vectwa,
"nbcomp": l_NumberOfComponentsPerPixelForPWAAndTWA
}
bintwa_app = OtbAppHandler("BinaryToVector",
param_bintwa,
write_output=True)
self._coarse_pipeline.add_otb_app(bintwa_app)
self.dict_of_vals["TWAImage"] = bintwa_app.getoutput()["out"]
# ********************************************************************************************************/
# * STO Reader connection */
# ********************************************************************************************************/
l_STOHeaderFilename = p_L2PrivateImageFilenamesProvider.get_sto_header_filename(
)
l_STOHandler = HeaderImageEarthExplorerXMLFileHandler(
l_STOHeaderFilename)
try:
self.STOBandTheoreticalWavelengthIsPresent = (
l_STOHandler.get_theoretical_wavelength() is not None)
self.STOBandTheoreticalWavelengthValue = l_STOHandler.get_theoretical_wavelength(
)
except BaseException:
LOGGER.debug("Could not retrive STO wavelength !!!")
# 4.3: Get the lis of date (in Bands nodes)
self.STOListOfStringDates = l_STOHandler.get_list_of_bands()
LOGGER.debug(
"L2ImageFileReaderBase::ReadPrivateImages - STO image filename: " +
p_L2PrivateImageFilenamesProvider.get_sto_image_filename() + ".")
tmp_sto_resample = os.path.join(working_dir, "tmp_sto_resample.tif")
app_sto_resample = resample(
p_L2PrivateImageFilenamesProvider.get_sto_image_filename(),
self._dem.ALC,
tmp_sto_resample,
OtbResampleType.BCO,
padradius=4.0,
write_output=False)
self._coarse_pipeline.add_otb_app(app_sto_resample)
tmp_sto_scale = os.path.join(working_dir, "tmp_sto_scale.tif")
param_scaled_sto = {
"im": app_sto_resample.getoutput().get("out"),
"coef": l_ReflectanceQuantificationValue,
"out": tmp_sto_scale
}
sto_scal_app = OtbAppHandler("MultiplyByScalar",
param_scaled_sto,
write_output=True)
self._coarse_pipeline.add_otb_app(sto_scal_app)
self.dict_of_vals["STOImage"] = sto_scal_app.getoutput()["out"]
# ********************************************************************************************************/
# * NDT Reader connection */
# ********************************************************************************************************/
LOGGER.debug(
"L2ImageFileReaderBase::ReadPrivateImages - NDT image filename: " +
p_L2PrivateImageFilenamesProvider.get_ndt_image_filename() + ".")
tmp_ndt_resample = os.path.join(working_dir, "tmp_ndt_resample.tif")
app_ndt_resample = resample(
p_L2PrivateImageFilenamesProvider.get_ndt_image_filename(),
self._dem.ALC,
tmp_ndt_resample,
OtbResampleType.BCO,
padradius=4.0,
threshold=0.25,
write_output=True)
self._coarse_pipeline.add_otb_app(app_ndt_resample)
self.dict_of_vals["NDTImage"] = app_ndt_resample.getoutput().get("out")
# 4-1-0 : HandlingConnectionCLD DM 1039
# #********************************************************************************************************/
# #* CLD sub (summary) Reader connection */
# #********************************************************************************************************/
LOGGER.debug(
"L2ImageFileReaderBase::ReadPrivateImages - CLD image filename: " +
p_L2PrivateImageFilenamesProvider.get_cld_image_filename() + ".")
# TODO ATTENTION m_CLDSubOutput est la sortie du Reader (donc pas toute le
# s 9 bandes algo, ou laors sortie du m_ReorganizeBinaryCLDSub ??!!!
self.dict_of_vals[
"CLDSubImage"] = p_L2PrivateImageFilenamesProvider.get_cld_image_filename(
)
# MACCS 5.0.0 : DM 982 . Need CLD bands (for example extract Shadow and ShadVar bands in L2CoarseResolution)
# For GetVectorizedCLDImageList method
tmp_cld_vec = os.path.join(working_dir, "tmp_cld_vec.tif")
param_vec_cld = {
"im": p_L2PrivateImageFilenamesProvider.get_cld_image_filename(),
"out": tmp_cld_vec,
"nbcomp": len(p_PluginBase.CLDCoreAlgorithmsMapBand)
}
cld_vec_app = OtbAppHandler("BinaryToVector",
param_vec_cld,
write_output=False)
self._coarse_pipeline.add_otb_app(cld_vec_app)
cld_vec_image = cld_vec_app.getoutput()["out"]
# In this case some cld bits are not available in the data
tmp_cld_zero = os.path.join(working_dir, "tmp_cld_zero.tif")
cld_const_zero_app = None
if len(p_PluginBase.CLDCoreAlgorithmsMapBand) > len(
p_PluginBase.CLDDataBandsSelected):
cld_const_zero_app = constant_image(cld_vec_image,
0,
os.path.join(
working_dir,
"tmp_zero_cld.tif"),
write_output=False)
self._coarse_pipeline.add_otb_app(cld_const_zero_app)
self.dict_of_vals["VectorizedCLDSubOutput"] = {}
for b in list(p_PluginBase.CLDCoreAlgorithmsMapBand.keys()):
if b in p_PluginBase.CLDDataBandsSelected:
tmp_cld_chan = os.path.join(working_dir, "tmp_" + b + ".tif")
chan = p_PluginBase.CLDDataBandsSelected.index(b)
app_extract_cld = extract_roi(cld_vec_image, [chan],
tmp_cld_chan,
write_output=False)
self._coarse_pipeline.add_otb_app(app_extract_cld)
tmp_cld_resample = os.path.join(working_dir,
"tmp_cld_resample.tif")
app_cld_resample = resample(
app_extract_cld.getoutput().get("out"),
self._dem.ALC,
tmp_cld_resample,
OtbResampleType.BCO,
padradius=4.0,
threshold=0.25,
write_output=True)
self._coarse_pipeline.add_otb_app(app_cld_resample)
self.dict_of_vals["VectorizedCLDSubOutput"][
b] = app_cld_resample.getoutput()["out"]
else:
tmp_cld_resample = os.path.join(working_dir,
"tmp_cld_resample.tif")
app_cld_resample = resample(
cld_const_zero_app.getoutput()["out"],
self._dem.ALC,
tmp_cld_resample,
OtbResampleType.BCO,
padradius=4.0,
threshold=0.25,
write_output=True)
self._coarse_pipeline.add_otb_app(app_cld_resample)
self.dict_of_vals["VectorizedCLDSubOutput"][
b] = app_cld_resample.getoutput()["out"]
# *************************************************************************************************************
# Generate a map of LUT containing all TOCR miniLUTs of the L2 product at D-1 (L2inTOCR)
# *************************************************************************************************************
if p_L2PrivateImageFilenamesProvider.get_dealing_ltc():
LOGGER.debug(
"L2ImageFileReaderBase::ReadPrivateImages - Start reading LTC ..."
)
l_Filename = p_L2PrivateImageFilenamesProvider.get_ltc_header_filename(
)
# --------------------------------------------
# Load the XML file
LOGGER.debug(
"L2ImageFileReaderBase::Start loading the LTC header filename <"
+ l_Filename + "> ....")
l_LUTXMLHandler = GippLUTEarthExplorerXMLFileHandler(l_Filename)
self.ListOfBandTheoreticalWavelengthOfTheLTCComposite = \
l_LUTXMLHandler.get_list_of_band_theoretical_wavelength()
# Read the files
l_L2P_LTC_ListOfFilenames = l_LUTXMLHandler.get_list_of_packaged_dbl_files(
True, True)
# convert the lut indexes
new_lut_indexes = IndexesType()
for ind, vals in list(l_LUTXMLHandler.get_lut_indexes().items()):
if ind == "Solar_Zenith_Angle_Indexes":
new_lut_indexes.set_Solar_Zenith_Angle_Indexes(vals)
elif ind == "Relative_Azimuth_Angle_Indexes":
new_lut_indexes.set_Relative_Azimuth_Angle_Indexes(vals)
elif ind == "Altitude_Indexes":
new_lut_indexes.set_Altitude_Indexes(vals)
elif ind == "AOT_Indexes":
new_lut_indexes.set_AOT_Indexes(vals)
elif ind == "TOA_Reflectance_Indexes":
new_lut_indexes.set_TOA_Reflectance_Indexes(vals)
elif ind == "Zenithal_Angle_Indexes":
new_lut_indexes.set_Zenithal_Angle_Indexes(vals)
elif ind == "View_Zenith_Angle_Indexes":
new_lut_indexes.set_View_Zenith_Angle_Indexes(vals)
elif ind == "Reflectance_Ratio_Indexes":
new_lut_indexes.set_Reflectance_Ratio_Indexes(vals)
else:
raise MajaDataException("Unhandled lut index type : " +
ind)
# Initialize the LookUpTable reader
# --------------------------------------------
# Set the list of minilut filenames to the mapReader
l_lutmap = LUTMap()
l_JdayRef = date_utils.get_julianday_as_int(
date_utils.get_datetime_from_yyyymmdd(
p_PluginBase.ReferenceDate))
for f in l_L2P_LTC_ListOfFilenames:
# Generate the LUT Map
# --------------------------------------------
l_jday = maja_utils.get_integer_jday_from_filename(f)
if l_jday < l_JdayRef:
raise MajaDataException(
"Cannot fill the LookUpTable with filename <" + f +
"> detected at Julian Day " + l_jday +
" as it's prior to reference date " +
p_PluginBase.ReferenceDate + " (" + l_JdayRef + ").")
l_jday = int(l_jday - l_JdayRef)
LOGGER.debug("l_jday " + str(l_jday) + " from filename " + f)
LOGGER.debug("l_JDayRef : " + str(l_JdayRef))
# Add cr lut to map
l_listoffile = List_Of_FilesType()
l_listoffile.add_Relative_File_Path("LTC_Lut_" + str(l_jday) +
".mha")
file_utils.copy_file(
f,
os.path.join(working_dir,
"LTC_Lut_" + str(l_jday) + ".mha"))
l_lut = LUT(index=str(l_jday),
Indexes=new_lut_indexes,
List_Of_Files=l_listoffile)
l_lutmap.add_LUT(l_lut)
# Write down the lut map
output = io.StringIO()
output.write('<?xml version="1.0" ?>\n')
l_lutmap.export(output,
0,
name_='LUTMap',
namespacedef_='',
pretty_print=True)
l_lutmap_filename = os.path.join(working_dir, "LTC_LutMap.xml")
with open(l_lutmap_filename, "w") as fh:
fh.write(output.getvalue().replace(" ", " "))
LOGGER.info("Writed new gipp lutmap to " + l_lutmap_filename)
output.close()
self.dict_of_vals["LTCImage"] = l_lutmap_filename
class VenusL1ImageFileReader(L1ImageReaderBase):
TOAFirstChannel = 1
TOALastChannel = 12
PIXChannel = 14
SATChannel = 13
PIXNumberOfComponentsPerPixel = 12
SATNumberOfComponentsPerPixel = 12
SOL1ChannelB1 = "?&sdataidx=0"
SOL1ChannelB2 = "?&sdataidx=1"
SOL2ChannelB1 = "?&sdataidx=2"
SOL2ChannelB2 = "?&sdataidx=3"
VIEB5ChannelB1 = "?&sdataidx=0"
VIEB5ChannelB2 = "?&sdataidx=1"
VIEB6ChannelB1 = "?&sdataidx=6"
VIEB6ChannelB2 = "?&sdataidx=7"
def __init__(self):
super(VenusL1ImageFileReader, self).__init__()
self._plugin = MajaVenusPlugin()
self._Satellite = "VEnUS"
self.headerHandler = None
self._l1toa_pipeline = OtbPipelineManager()
self._l1pix_pipeline = OtbPipelineManager()
self._l2pix_pipeline = OtbPipelineManager()
self._edg_pipeline = OtbPipelineManager()
self._l2toa_pipeline = OtbPipelineManager()
self._l2edg_pipeline = OtbPipelineManager()
self._sat_pipeline = OtbPipelineManager()
self._sol_pipeline = OtbPipelineManager()
self._dtmvie_pipeline = OtbPipelineManager()
self._shadowvie_pipeline = OtbPipelineManager()
self._toascalar = ""
self._l1pix = ""
self._l2pix = ""
self._edgsub = ""
self._toasub = ""
self._l2toa = ""
self._l2edg = ""
self._l2sat = ""
self._satsub = ""
self._cla = ""
self._sol1 = ""
self._sol2 = ""
self._dtmvie = ""
self._shadowvie = ""
self._reall1nodata = -10
self._meanZenithMap = {}
self._meanAzimuthMap = {}
def can_read(self, plugin_name):
return plugin_name == "VENUS"
def read(self, product_info, app_handler, l2comm, dem, pReadL1Mode):
"""product_info,plugin, l2comm,mode
:param product_info: L1ImageInformationsBase
:param pReadL1Mode: ReadL1ModeType
:return:
"""
LOGGER.debug("Start Venus L1 ImageFileReader ...")
product_filename = product_info.HeaderFilename
LOGGER.debug("Start Venus L1 ImageFileReader with the filename: " +
product_filename)
self._plugin.initialize(app_handler)
self.headerHandler = product_info.HeaderHandler
self._dem = dem
# working_dir = get_working_directory("L1Read_", app_handler.get_working_directory())
working_dir = app_handler.get_directory_manager(
).get_temporary_directory("L1Read_", do_always_remove=True)
self.ReadL1Mode = pReadL1Mode
self._GIPPL2COMMHandler = l2comm
l_BandsDefinitions = self._plugin.BandsDefinitions
# --------------------------------------
# Initialize the Image filename provider
l_FilenameProvider = VenusL1ImageFilenames()
#product_info.FilenamesProvider
IsValidSatellite = (l_FilenameProvider.initialize(product_filename)
is not False)
if not IsValidSatellite:
raise MajaPluginVenusException(
"The file <{}> is not a valid Venus L1 product.".format(
product_filename))
# *********************************************************************************************************
# Register the Header o the Input image file
# *********************************************************************************************************
LOGGER.debug("Load the Venus L1 Header file : '" + product_filename +
"'")
l_L1XMLHandler = HeaderImageEarthExplorerXMLFileHandler(
product_filename)
# *********************************************************************************************************
# TOA Reader connection
# *********************************************************************************************************
l_L1NoData = l_L1XMLHandler.get_no_data_value_as_double()
l_ReflectanceQuantificationValue = l_L1XMLHandler.get_reflectance_quantification_value(
)
l_reflectanceMultiplicationValues = []
if xml_tools.as_bool(l2comm.get_value("CalAdjustOption")):
l_factor = xml_tools.as_float_list(
l2comm.get_value("CalAdjustFactor"))
if len(l_factor) != (VenusL1ImageFileReader.TOALastChannel -
VenusL1ImageFileReader.TOAFirstChannel + 1):
raise MajaPluginVenusException(
"Not the same number of Calibration coeffs than L1 bands")
for i in range(0, len(l_factor)):
l_reflectanceMultiplicationValues.append(
l_ReflectanceQuantificationValue * l_factor[i])
else:
for i in range(VenusL1ImageFileReader.TOALastChannel -
VenusL1ImageFileReader.TOAFirstChannel + 1):
l_reflectanceMultiplicationValues.append(
l_ReflectanceQuantificationValue)
l_RealL1NoData = l_L1NoData * l_ReflectanceQuantificationValue
# *********************************************************************************************************
# L1 TOA image pipeline connection
# *********************************************************************************************************
if not l_FilenameProvider.m_TOAImageFileName:
raise MajaPluginVenusException(
"VenusL1ImageFileReader : The TOA image does not exist !")
tmp_l1toa_roi = os.path.join(working_dir, "l1toa_roi.tif")
app_l1_toa_roi = extract_roi(l_FilenameProvider.m_TOAImageFileName, [
channel
for channel in range(VenusL1ImageFileReader.TOALastChannel -
VenusL1ImageFileReader.TOAFirstChannel + 1)
],
tmp_l1toa_roi,
write_output=False)
self._l1toa_pipeline.add_otb_app(app_l1_toa_roi)
tmp_sat_roi = os.path.join(working_dir, "sat_roi.tif")
app_sat_roi = extract_roi(l_FilenameProvider.m_TOAImageFileName,
[VenusL1ImageFileReader.SATChannel - 1],
tmp_sat_roi,
write_output=False)
self._sat_pipeline.add_otb_app(app_sat_roi)
#Multiply scalar by quantif
tmp_l1toa_mbs = os.path.join(working_dir, "l1toa.tif")
app_l1toa_mbs = multiply_by_scalar(
app_l1_toa_roi.getoutput().get("out"),
l_ReflectanceQuantificationValue,
output_image=tmp_l1toa_mbs,
write_output=False)
self._l1toa_pipeline.add_otb_app(app_l1toa_mbs)
# update all extract ROI in once
write_images([
app_l1toa_mbs.getoutput().get("out"),
app_sat_roi.getoutput().get("out")
], [tmp_l1toa_mbs, tmp_sat_roi])
self._toascalar = tmp_l1toa_mbs
# *********************************************************************************************************
# L1 PIX image pipeline connection
# *********************************************************************************************************
tmp_l1pix_roi = os.path.join(working_dir, "l1pix.tif")
app_l1_pix_roi = extract_roi(l_FilenameProvider.m_TOAImageFileName,
[VenusL1ImageFileReader.PIXChannel - 1],
tmp_l1pix_roi + ":uint16",
write_output=False)
self._l1pix_pipeline.add_otb_app(app_l1_pix_roi)
self._l1pix = app_l1_pix_roi.getoutput().get("out")
# *********************************************************************************************************
# START READ L1 for ALGORITHMS
# *********************************************************************************************************
if pReadL1Mode == ReadL1Mode.READ_L1_MODE_FOR_ALGORITHMS:
# *********************************************************************************************************
# L2 PIX image pipeline connection
# *********************************************************************************************************
# LAIG-FA-MAC-131720-CS : New for 4.2
# Before resample, binarytovector -> resample -> vectortobinary
tmp_l2pix_bin2vec = os.path.join(working_dir, "l2pix_bin2vec.tif")
param_l2pix_bin2vec = {
"im": app_l1_pix_roi.getoutput().get("out"),
"out": tmp_l2pix_bin2vec + ":uint8",
"nbcomp": VenusL1ImageFileReader.PIXNumberOfComponentsPerPixel
}
app_l2pix_bin2vec = OtbAppHandler("BinaryToVector",
param_l2pix_bin2vec,
write_output=False)
self._l2pix_pipeline.add_otb_app(app_l2pix_bin2vec)
tmp_l2pix_resample = os.path.join(working_dir,
"l2pix_resample.tif")
app_l2pix_resample = resample(
app_l2pix_bin2vec.getoutput().get("out"),
self._dem.ALTList[0],
tmp_l2pix_resample,
OtbResampleType.LINEAR_WITH_RADIUS,
padradius=4.0,
threshold=0.0,
write_output=False)
self._l2pix_pipeline.add_otb_app(app_l2pix_resample)
#L2 PIX is concatenate
tmp_l2pix_binconcat = os.path.join(working_dir, "l2pix.tif")
param_l2pix_binconcat = {
"im": app_l2pix_resample.getoutput().get("out"),
"out": tmp_l2pix_binconcat + ":uint16"
}
app_l2pix_binconcat = OtbAppHandler("BinaryConcatenate",
param_l2pix_binconcat,
write_output=False)
self._l2pix = app_l2pix_binconcat.getoutput().get("out")
self._l2pix_pipeline.add_otb_app(app_l2pix_binconcat)
# *********************************************************************************************************
# L2 EDG image pipeline connection
# *********************************************************************************************************
tmp_edg_thresholder = os.path.join(working_dir,
"edg_thresholder1.tif")
param_edg_thresholder1 = {
"im": self._toascalar,
"thresholdvalue": l_RealL1NoData,
"equalvalue": 255,
"outsidevalue": 0,
"out": tmp_edg_thresholder + ":uint8"
}
app_edg_thresholder1 = OtbAppHandler("OneBandEqualThreshold",
param_edg_thresholder1,
write_output=True)
self._edg_pipeline.add_otb_app(app_edg_thresholder1)
tmp_edg_resample = os.path.join(working_dir, "edg_resample.tif")
app_edg_resample = resample(
app_edg_thresholder1.getoutput().get("out"),
self._dem.ALTList[0],
tmp_edg_resample,
OtbResampleType.BCO,
padradius=4.0,
write_output=True)
self._edg_pipeline.add_otb_app(app_edg_resample)
# Threshold the output
out_sub_edg = os.path.join(working_dir, "edg_thresholder2.tif")
param_edg_thresholder2 = {
"im": app_edg_resample.getoutput().get("out"),
"thresholdvalue": 0,
"equalvalue": 1,
"outsidevalue": 0,
"out": out_sub_edg + ":uint8"
}
app_edg_thresholder2 = OtbAppHandler("OneBandEqualThreshold",
param_edg_thresholder2,
write_output=True)
self._edg_pipeline.add_otb_app(app_edg_thresholder2)
# *********************************************************************************************************
# IPEDGSub image pipeline connection
# *********************************************************************************************************
tmp_edgsub_resample = os.path.join(working_dir,
"edgsub_resample.tif")
app_edgsub_resample = resample(
app_edg_thresholder1.getoutput().get("out"),
self._dem.ALC,
tmp_edgsub_resample,
OtbResampleType.LINEAR_WITH_RADIUS,
padradius=12.0,
write_output=True)
self._edg_pipeline.add_otb_app(app_edgsub_resample)
# Threshold the output
out_sub_edgsub = os.path.join(working_dir, "edgsub.tif")
param_edgsub_thresholder2 = {
"im": app_edgsub_resample.getoutput().get("out"),
"thresholdvalue": 0,
"equalvalue": 0,
"outsidevalue": 1,
"out": out_sub_edgsub + ":uint8"
}
app_edgsub_thresholder2 = OtbAppHandler("OneBandEqualThreshold",
param_edgsub_thresholder2,
write_output=True)
self._edgsub = app_edgsub_thresholder2.getoutput().get("out")
self._edg_pipeline.add_otb_app(app_edgsub_thresholder2)
# *********************************************************************************************************
# L2 TOA image pipeline connection
# *********************************************************************************************************
tmp_l2toa_resample = os.path.join(working_dir,
"l2toa_resample.tif")
app_l2toa_resample = resample(self._toascalar,
self._dem.ALTList[0],
tmp_l2toa_resample,
OtbResampleType.BCO,
padradius=4.0,
write_output=False)
self._l2toa_pipeline.add_otb_app(app_l2toa_resample)
l2toa_list = []
l_toathresholdminvalue = 0
l_toathresholvalue = -10
#Apply EDG mask on l2toa resampled
tmp_l2toa = os.path.join(working_dir, "l2toa.tif")
app_l2toa = apply_mask(app_l2toa_resample.getoutput().get("out"),
app_edg_thresholder2.getoutput().get("out"),
l_toathresholvalue,
tmp_l2toa,
write_output=False)
self._l2toa = app_l2toa.getoutput().get("out")
self._l2toa_pipeline.add_otb_app(app_l2toa)
# *********************************************************************************************************
# TOA Sub image pipeline connection
# *********************************************************************************************************
tmp_toasub_resample = os.path.join(working_dir,
"toasub_resample.tif")
app_toasub_resample = resample(self._toascalar,
self._dem.ALC,
tmp_toasub_resample,
OtbResampleType.LINEAR_WITH_RADIUS,
padradius=4.0,
write_output=True)
self._l2toa_pipeline.add_otb_app(app_toasub_resample)
# Threshold the output
out_edgsub_threshold = os.path.join(working_dir,
"edgsubthreshold.tif")
param_edgsub_threshold = {
"im": app_edgsub_resample.getoutput().get("out"),
"thresholdvalue": 0,
"equalvalue": 1,
"outsidevalue": 0,
"out": out_edgsub_threshold + ":uint8"
}
app_edgsub_threshold = OtbAppHandler("OneBandEqualThreshold",
param_edgsub_threshold,
write_output=True)
self._edg_pipeline.add_otb_app(app_edgsub_threshold)
tmp_l2subtoa = os.path.join(working_dir, "toasub.tif")
app_l2subtoa = apply_mask(
app_toasub_resample.getoutput().get("out"),
app_edgsub_threshold.getoutput().get("out"),
l_toathresholvalue,
tmp_l2subtoa,
write_output=True)
self._toasub = app_l2subtoa.getoutput().get("out")
self._l2toa_pipeline.add_otb_app(app_l2subtoa)
# *********************************************************************************************************
# L2EDG - Actualization of the L2 edge mask
# *********************************************************************************************************
#tmp_l2edg_threshold = os.path.join(working_dir, "l2edg_threshold.tif")
#app_l2edg_threshold = binary_threshold(self._edgsub,
# lower_threshold=0,
# inside_value=1000,
# outside_value=0,
# output_image=tmp_l2edg_threshold + ":uint8",
# write_output=True)
#self._l2edg_pipeline.add_otb_app(app_l2edg_threshold)
tmp_l2edg_resample = os.path.join(working_dir, "l2edg.tif")
app_l2edg_resample = resample(self._edgsub,
self._dem.ALTList[0],
tmp_l2edg_resample + ":uint8",
OtbResampleType.LINEAR,
padradius=4.0,
threshold=0.001,
write_output=True)
self._l2edg = app_l2edg_resample.getoutput().get("out")
self._l2edg_pipeline.add_otb_app(app_l2edg_resample)
# *********************************************************************************************************
# SAT image pipeline connection
# *********************************************************************************************************
tmp_sat_bin2vec = os.path.join(working_dir, "sat_bin2vec.tif")
param_sat_bin2vec = {
"im": tmp_sat_roi,
"out": tmp_sat_bin2vec + ":uint8",
"nbcomp": VenusL1ImageFileReader.SATNumberOfComponentsPerPixel
}
app_sat_bin2vec = OtbAppHandler("BinaryToVector",
param_sat_bin2vec,
write_output=False)
self._sat_pipeline.add_otb_app(app_sat_bin2vec)
l_l2sat_thresholdvalue = l2comm.get_value_f("SaturationThreshold")
tmp_sat_resample = os.path.join(working_dir, "l2sat.tif")
app_sat_resample = resample(app_sat_bin2vec.getoutput().get("out"),
self._dem.ALTList[0],
tmp_sat_resample + ":uint8",
OtbResampleType.BCO,
padradius=4.0,
threshold=l_l2sat_thresholdvalue,
write_output=False)
self._l2sat = app_sat_resample.getoutput().get("out")
self._sat_pipeline.add_otb_app(app_sat_resample)
# *********************************************************************************************************
# IPSAT Sub image pipeline connection
# *********************************************************************************************************
l_sat_subthresholdvalue = l2comm.get_value_f(
"SaturationThresholdSub")
tmp_satsub_resample = os.path.join(working_dir, "satsub.tif")
app_satsub_resample = resample(
app_sat_bin2vec.getoutput().get("out"),
self._dem.ALC,
tmp_satsub_resample + ":uint8",
OtbResampleType.LINEAR_WITH_RADIUS,
padradius=4.0,
threshold=l_sat_subthresholdvalue)
self._satsub = app_satsub_resample.getoutput().get("out")
self._sat_pipeline.add_otb_app(app_satsub_resample)
# *********************************************************************************************************
# CLA image pipeline connection
# *********************************************************************************************************
LOGGER.debug(
"VenusL1ImageFileReader::Initialize - CLA image filename: '" +
l_FilenameProvider.m_CLAImageFileName + "'")
if not l_FilenameProvider.m_CLAImageFileName:
raise MajaPluginVenusException(
"The CLA image does not exist !! ")
self._cla = l_FilenameProvider.m_CLAImageFileName
# *********************************************************************************************************
# SOL1 image pipeline connection
# *********************************************************************************************************
LOGGER.debug(
"VenusL1ImageFileReader::Initialize - SOL image filename: '" +
l_FilenameProvider.m_SOLImageFileName + "'")
if not l_FilenameProvider.m_SOLImageFileName:
raise MajaPluginVenusException(
"The SOL image does not exist !! ")
mtdat = GdalDatasetInfo(l_FilenameProvider.m_TOAImageFileName)
toaarea = Area()
toaarea.size = mtdat.size
toaarea.origin = mtdat.origin
toaarea.spacing = mtdat.pixel_size
l_SOLHeaderHandler = HeaderImageEarthExplorerXMLFileHandler(
l_FilenameProvider.m_SOLHeaderFileName)
l_L1SOLSubsamplingFactor = l_SOLHeaderHandler.get_sampling_factor()
LOGGER.debug(l_L1SOLSubsamplingFactor)
# SOL1
tmp_sol1_b1 = os.path.join(working_dir, "sol1_B1.tif")
app_sol1_b1 = multiply_by_scalar(
l_FilenameProvider.m_SOLImageFileName +
VenusL1ImageFileReader.SOL1ChannelB1,
toaarea.spacing[0],
tmp_sol1_b1,
write_output=False)
self._sol_pipeline.add_otb_app(app_sol1_b1)
tmp_sol1_b2 = os.path.join(working_dir, "sol1_B2.tif")
app_sol1_b2 = multiply_by_scalar(
l_FilenameProvider.m_SOLImageFileName +
VenusL1ImageFileReader.SOL1ChannelB2,
(-1) * toaarea.spacing[1],
tmp_sol1_b2,
write_output=False)
self._sol_pipeline.add_otb_app(app_sol1_b2)
tmp_sol1_concat = os.path.join(working_dir, "sol1_concat.tif")
param_sol1_concat = {
"il": [
app_sol1_b1.getoutput().get("out"),
app_sol1_b2.getoutput().get("out")
],
"out":
tmp_sol1_concat
}
app_sol1_concat = OtbAppHandler("ConcatenateDoubleImages",
param_sol1_concat)
update_projection(l_FilenameProvider.m_TOAImageFileName,
app_sol1_concat.getoutput().get("out"),
l_L1SOLSubsamplingFactor)
self._sol_pipeline.add_otb_app(app_sol1_concat)
tmp_sol1_resample = os.path.join(working_dir, "sol1.tif")
app_sol1_resample = resample(
app_sol1_concat.getoutput().get("out"),
self._dem.ALC,
tmp_sol1_resample,
OtbResampleType.LINEAR,
padradius=4.0)
self._sol1 = app_sol1_resample.getoutput().get("out")
self._sol_pipeline.add_otb_app(app_sol1_resample)
# SOL2
tmp_sol2_b1 = os.path.join(working_dir, "sol2_B1.tif")
app_sol2_b1 = multiply_by_scalar(
l_FilenameProvider.m_SOLImageFileName +
VenusL1ImageFileReader.SOL2ChannelB1,
toaarea.spacing[0],
tmp_sol2_b1,
write_output=False)
self._sol_pipeline.add_otb_app(app_sol2_b1)
tmp_sol2_b2 = os.path.join(working_dir, "sol2_B2.tif")
app_sol2_b2 = multiply_by_scalar(
l_FilenameProvider.m_SOLImageFileName +
VenusL1ImageFileReader.SOL2ChannelB2,
(-1) * toaarea.spacing[1],
tmp_sol2_b2,
write_output=False)
self._sol_pipeline.add_otb_app(app_sol2_b2)
tmp_sol2_concat = os.path.join(working_dir, "sol2_concat.tif")
param_sol2_concat = {
"il": [
app_sol2_b1.getoutput().get("out"),
app_sol2_b2.getoutput().get("out")
],
"out":
tmp_sol2_concat
}
app_sol2_concat = OtbAppHandler("ConcatenateDoubleImages",
param_sol2_concat)
update_projection(l_FilenameProvider.m_TOAImageFileName,
app_sol2_concat.getoutput().get("out"),
l_L1SOLSubsamplingFactor)
self._sol_pipeline.add_otb_app(app_sol2_concat)
tmp_sol2_resample = os.path.join(working_dir, "sol2.tif")
app_sol2_resample = resample(
app_sol2_concat.getoutput().get("out"),
self._dem.ALC,
tmp_sol2_resample,
OtbResampleType.LINEAR,
padradius=4.0)
self._sol2 = app_sol2_resample.getoutput().get("out")
self._sol_pipeline.add_otb_app(app_sol2_resample)
# *********************************************************************************************************
# DTMVIE image pipeline connection
# *********************************************************************************************************
LOGGER.debug(
"VenusL1ImageFileReader::Initialize - VIE image filename: '" +
l_FilenameProvider.m_VIEImageFileName + "'")
l_VIEHeaderHandler = HeaderImageEarthExplorerXMLFileHandler(
l_FilenameProvider.m_VIEHeaderFileName)
l_L1VIESubsamplingFactor = l_VIEHeaderHandler.get_sampling_factor()
LOGGER.debug(l_L1VIESubsamplingFactor)
tmp_vieb5b1_mult = os.path.join(working_dir, "vie5b1_mult.tif")
app_vieb5b1_mult = multiply_by_scalar(
l_FilenameProvider.m_VIEImageFileName +
VenusL1ImageFileReader.VIEB5ChannelB1,
toaarea.spacing[0],
tmp_vieb5b1_mult,
write_output=False)
self._dtmvie_pipeline.add_otb_app(app_vieb5b1_mult)
tmp_vieb5b2_mult = os.path.join(working_dir, "vie5b2_mult.tif")
app_vieb5b2_mult = multiply_by_scalar(
l_FilenameProvider.m_VIEImageFileName +
VenusL1ImageFileReader.VIEB5ChannelB2,
(-1) * toaarea.spacing[1],
tmp_vieb5b2_mult,
write_output=False)
self._dtmvie_pipeline.add_otb_app(app_vieb5b2_mult)
tmp_vieb6b1_mult = os.path.join(working_dir, "vie6b1_mult.tif")
app_vieb6b1_mult = multiply_by_scalar(
l_FilenameProvider.m_VIEImageFileName +
VenusL1ImageFileReader.VIEB6ChannelB1,
toaarea.spacing[0],
tmp_vieb6b1_mult,
write_output=False)
self._dtmvie_pipeline.add_otb_app(app_vieb6b1_mult)
tmp_vieb6b2_mult = os.path.join(working_dir, "vie6b2_mult.tif")
app_vieb6b2_mult = multiply_by_scalar(
l_FilenameProvider.m_VIEImageFileName +
VenusL1ImageFileReader.VIEB6ChannelB2,
(-1) * toaarea.spacing[1],
tmp_vieb6b2_mult,
write_output=False)
self._dtmvie_pipeline.add_otb_app(app_vieb6b2_mult)
tmp_dtmvie_concat = os.path.join(working_dir, "dtmvie_concat.tif")
param_dtmvie_concat = {
"il": [
app_vieb5b1_mult.getoutput().get("out"),
app_vieb5b2_mult.getoutput().get("out"),
app_vieb6b1_mult.getoutput().get("out"),
app_vieb6b2_mult.getoutput().get("out")
],
"out":
tmp_dtmvie_concat
}
app_dtmvie_concat = OtbAppHandler("ConcatenateDoubleImages",
param_dtmvie_concat)
update_projection(l_FilenameProvider.m_TOAImageFileName,
app_dtmvie_concat.getoutput().get("out"),
l_L1VIESubsamplingFactor)
self._dtmvie_pipeline.add_otb_app(app_dtmvie_concat)
tmp_dtmvie_resample = os.path.join(working_dir, "dtmvie.tif")
app_dtmvie_resample = resample(
app_dtmvie_concat.getoutput().get("out"),
self._dem.ALC,
tmp_dtmvie_resample,
OtbResampleType.LINEAR,
padradius=4.0)
self._dtmvie = app_dtmvie_resample.getoutput().get("out")
self._dtmvie_pipeline.add_otb_app(app_dtmvie_resample)
# *********************************************************************************************************
# VIE image pipeline connection
# *********************************************************************************************************
tmp_shadowvie_concat = os.path.join(working_dir,
"shadowvie_concat.tif")
param_shadowvie_concat = {
"il": [
app_vieb5b1_mult.getoutput().get("out"),
app_vieb5b2_mult.getoutput().get("out")
],
"out":
tmp_shadowvie_concat
}
app_shadowvie_concat = OtbAppHandler("ConcatenateDoubleImages",
param_shadowvie_concat)
self._shadowvie_pipeline.add_otb_app(app_shadowvie_concat)
update_projection(l_FilenameProvider.m_TOAImageFileName,
app_shadowvie_concat.getoutput().get("out"),
l_L1VIESubsamplingFactor)
tmp_shadowvie_resample = os.path.join(working_dir, "shadowvie.tif")
app_shadowvie_resample = resample(
app_shadowvie_concat.getoutput().get("out"),
self._dem.ALC,
tmp_shadowvie_resample,
OtbResampleType.LINEAR,
padradius=4.0)
self._shadowvie = app_shadowvie_resample.getoutput().get("out")
self._shadowvie_pipeline.add_otb_app(app_shadowvie_resample)
# Fill the datas
self.dict_of_vals["IPEDGSubOutput"] = self._edgsub
self.dict_of_vals["SOL1Image"] = self._sol1
self.dict_of_vals["SOL2Image"] = self._sol2
self.dict_of_vals["DTMVIEImage"] = self._dtmvie
self.dict_of_vals["IPTOASubOutput"] = self._toasub
self.dict_of_vals["L2TOAImageList"] = [self._l2toa]
self.dict_of_vals["ViewingZenithMeanMap"] = self._meanZenithMap
self.dict_of_vals["ViewingAzimuthMeanMap"] = self._meanAzimuthMap
self.dict_of_vals["CLAImage"] = self._cla
self.dict_of_vals["IPSATSubOutput"] = self._satsub
self.dict_of_vals["ShadowVIEImage"] = self._shadowvie
if self._plugin.CirrusMasking:
l_CirrusBandCode = l2comm.get_value("CirrusBandCode")
l_CirrusBandIdx = self._plugin.BandsDefinitions.get_band_id_in_l2_coarse(
l_CirrusBandCode)
tmp = os.path.join(working_dir, "l1toacirrus.tif")
app = extract_roi(self._toascalar, [l_CirrusBandIdx - 1], tmp)
self.dict_of_vals["L1TOACirrusImage"] = app.getoutput().get(
"out")
self.dict_of_vals["L2EDGOutputList"] = [self._l2edg]
self.dict_of_vals["L2SATImageList"] = [self._l2sat]
self.dict_of_vals["L2PIXImageList"] = [self._l2pix]
self.dict_of_vals["L1PIXImageList"] = [self._l1pix]
self.dict_of_vals["L1TOAImageList"] = [self._toascalar]
class Landsat8L1ImageFileReader(L1ImageReaderBase):
def __init__(self):
super(Landsat8L1ImageFileReader, self).__init__()
self._Satellite = "LANDSAT_8"
self._plugin = MajaLandsat8Plugin()
self._header_handler = None
self._toascalarlist = []
self._satmasklist = []
self._l1toaimagelist = []
self._l2piximagelist = []
self._l2toaimagelist = []
self._l2satmasklist = []
self._qbmask = ""
self._edgsubmask = ""
self._subtoaimage = ""
self._subsatimage = ""
self._claimage = ""
self._meanZenithMap = {}
self._meanAzimuthMap = {}
self._sol1image = ""
self._vieimage = []
self._l2edgmasklist = []
self._toasublist = []
self._satsublist = []
self._pix_pipeline = OtbPipelineManager()
self._qb_pipeline = OtbPipelineManager()
self._toa_pipeline = OtbPipelineManager()
self._toa_sub_pipeline = OtbPipelineManager()
self._l2toa_pipeline = OtbPipelineManager()
self._edg_pipeline = OtbPipelineManager()
self._sat_pipeline = OtbPipelineManager()
self._sat_sub_pipeline = OtbPipelineManager()
self._sol1_pipeline = OtbPipelineManager()
self._vie_pipeline = OtbPipelineManager()
self._reall1nodata = -10
self._reall2nodata = 0
self._toathresholdminvalue = 0
def can_read(self, plugin_name):
return plugin_name == "LANDSAT8"
def generate_pix_mask(self, working_dir):
LOGGER.debug("Landsat8L1ImageFileReader::GeneratePIXMask()")
l_BandsDefinitions = self._plugin.BandsDefinitions
l_ListOfL1Resolution = l_BandsDefinitions.ListOfL1Resolution # ListOfStrings
for l1res in range(0, len(l_ListOfL1Resolution)):
curL1Res = l_ListOfL1Resolution[l1res]
dtm = self._dem.ALTList[l1res]
tmp_constant_filename = os.path.join(
working_dir, "PIX_Masks_const_{}.tif".format(curL1Res))
app = constant_image(dtm,
0,
tmp_constant_filename + ":uint8",
write_output=False)
self._l2piximagelist.append(app.getoutput().get("out"))
self._pix_pipeline.add_otb_app(app)
def generate_qb_mask(self, qb_filename, working_dir):
img_binaryqb = os.path.join(working_dir, "NoDataFromQBFilter.tif")
param_bintovec_qb = {
"im": qb_filename,
"out": img_binaryqb + ":uint8",
"nbcomp": 16
}
app = OtbAppHandler("BinaryToVector",
param_bintovec_qb,
write_output=False)
self._toa_pipeline.add_otb_app(app)
self._qbmask = app.getoutput().get("out")
self._qb_pipeline.add_otb_app(app)
def generate_l1_toa_image(self, l2comm, factor, solzenith, L1ResolList,
working_dir):
for l1res in range(len(L1ResolList)):
curL1Res = L1ResolList[l1res]
l_ListOfL1BandCode = self._plugin.BandsDefinitions.get_list_of_l1_band_code(
curL1Res)
for band in l_ListOfL1BandCode:
curMULT = self._header_handler.get_ref_mutl_factor_from_band_code(
band)
curADD = self._header_handler.get_ref_add_factor_from_band_code(
band)
expression_bandmath = "(im2b1 || (im1b1-0.00001)<0)?" + str(
self._reall1nodata) + ":"
if xml_tools.as_bool(l2comm.get_value("CalAdjustOption")):
expression_bandmath = expression_bandmath + str(
factor) + "*"
expression_bandmath = expression_bandmath + "(im1b1*" + str(
curMULT) + "+" + str(curADD) + ")/cos(" + str(
solzenith) + "*" + str(math.pi / 180) + ")"
img_toaconverter = os.path.join(
working_dir, "TOAConverter_{}.tif".format(band))
app_bandmath = band_math([
self._header_handler.get_toa_image_filename_from_band_code(
band), self._qbmask
],
expression_bandmath,
img_toaconverter,
write_output=False)
self._toascalarlist.append(app_bandmath.getoutput().get("out"))
self._toa_pipeline.add_otb_app(app_bandmath)
img_concatenate_toa = os.path.join(working_dir,
"l1toa_{}.tif".format(l1res))
param_concatenate_toa = {
"il": self._toascalarlist,
"out": img_concatenate_toa
}
app_concatenate_toa = OtbAppHandler("ConcatenateDoubleImages",
param_concatenate_toa,
write_output=True)
self._l1toaimagelist.append(app_concatenate_toa.getoutput()["out"])
self._qb_pipeline.free_otb_app()
self._toa_pipeline.free_otb_app()
def generate_toa_sub_image(self, working):
dtm_coarse = self._dem.ALC
# For each res of the input L1 product
for i, toa in enumerate(self._l1toaimagelist):
# undersampling at L2CoarseResolution
toa_sub_image = os.path.join(working, "aot_sub_{}.tif".format(i))
app_resample = resample(toa,
dtm_coarse,
toa_sub_image,
OtbResampleType.LINEAR_WITH_RADIUS,
write_output=False)
self._toa_sub_pipeline.add_otb_app(app_resample)
self._toasublist.append(app_resample.getoutput().get("out"))
# end res loop
#concat
toa_sub_image = os.path.join(working, "aot_sub.tif")
param_concatenate = {"il": self._toasublist, "out": toa_sub_image}
app_concatenate = OtbAppHandler("ConcatenateDoubleImages",
param_concatenate,
write_output=False)
self._toa_sub_pipeline.add_otb_app(app_concatenate)
#apply edg
#invert EDG for masking
out_edgsub_threshold = os.path.join(working, "edgsubthreshold.tif")
param_edgsub_threshold = {
"im": self._edgsubmask,
"thresholdvalue": self._toathresholdminvalue,
"equalvalue": 1,
"outsidevalue": 0,
"out": out_edgsub_threshold + ":uint8"
}
app_edgsub_threshold = OtbAppHandler("OneBandEqualThreshold",
param_edgsub_threshold,
write_output=False)
self._toa_sub_pipeline.add_otb_app(app_edgsub_threshold)
#apply mask to sub toa
tmp_l2subtoa = os.path.join(working, "toasub.tif")
app_l2subtoa = apply_mask(app_concatenate.getoutput().get("out"),
app_edgsub_threshold.getoutput().get("out"),
self._reall1nodata,
tmp_l2subtoa,
write_output=True)
self._subtoaimage = app_l2subtoa.getoutput().get("out")
self._toa_sub_pipeline.free_otb_app()
def generate_l2_toa_images(self, l_ListOfL2Resolution, working_dir):
for l1res in range(len(l_ListOfL2Resolution)):
curL1Res = l_ListOfL2Resolution[l1res]
dtm = self._dem.ALTList[l1res]
l_ListOfL2BandCode = self._plugin.BandsDefinitions.get_list_of_l2_band_code(
curL1Res)
LOGGER.debug("Read the nb bands" + str(len(l_ListOfL2BandCode)) +
" of the TOA images...")
l_l1bandidx = [
self._plugin.BandsDefinitions.get_band_id_in_l1(b)
for b in l_ListOfL2BandCode
]
tmp_toa_resample = os.path.join(working_dir,
"tmp_toa_resample.tif")
app_toa_resample = resample(self._l1toaimagelist[l1res],
dtm,
tmp_toa_resample,
OtbResampleType.LINEAR_WITH_RADIUS,
write_output=False)
self._l2toa_pipeline.add_otb_app(app_toa_resample)
# L2TOA has less band than L1 so extract
tmp_toa_roi = os.path.join(
working_dir, "L2TOAImageList_{}.tif".format(curL1Res))
l2toa_roi_app = extract_roi(
app_toa_resample.getoutput().get("out"),
l_l1bandidx,
tmp_toa_roi,
write_output=False)
self._l2toa_pipeline.add_otb_app(l2toa_roi_app)
self._l2toaimagelist.append(l2toa_roi_app.getoutput().get("out"))
def generate_edg_images(self, working_dir):
dtm_coarse = self._dem.ALC
# One band equal threshold
out_edg = os.path.join(working_dir, "tmp_EDG_oneBandEqual.tif")
param_edg = {
"im": self._l1toaimagelist[0],
"thresholdvalue": self._reall1nodata,
"equalvalue": 255,
"outsidevalue": 0,
"out": out_edg + ":double"
}
onebandequal_app = OtbAppHandler("OneBandEqualThreshold",
param_edg,
write_output=False)
self._edg_pipeline.add_otb_app(onebandequal_app)
# Resample to coarse
LOGGER.debug("Start IPEDGSub.")
tmp_edg_sub_resample = os.path.join(working_dir, "tmp_edg_sub.tif")
edg_sub_resample_app = resample(
onebandequal_app.getoutput().get("out"),
dtm_coarse,
tmp_edg_sub_resample,
OtbResampleType.LINEAR_WITH_RADIUS,
padradius=4.0,
write_output=False)
# Threshold the output
out_sub_edg = os.path.join(working_dir, "IPEDGSubOutput.tif")
param_sub_edg = {
"im": edg_sub_resample_app.getoutput().get("out"),
"thresholdvalue": 0,
"equalvalue": 0,
"outsidevalue": 1,
"out": out_sub_edg + ":uint8"
}
onebandequal_sub_app = OtbAppHandler("OneBandEqualThreshold",
param_sub_edg,
write_output=True)
# Put in internal data
self._edgsubmask = onebandequal_sub_app.getoutput().get("out")
LOGGER.debug("Start IPEDGSub done.")
# *******************************************************************************************************
# L2EDG pipeline connection
# *******************************************************************************************************
LOGGER.debug("Start L2EDG ...")
l_BandsDefinitions = self._plugin.BandsDefinitions
l_ListOfL2Resolution = l_BandsDefinitions.ListOfL2Resolution # ListOfStrings
# At L2 resolution
l_NbL2Res = len(l_ListOfL2Resolution)
for r in range(l_NbL2Res):
res_str = l_ListOfL2Resolution[r]
# ExpandFilterPointer => PadAndResampleImageFilter => app ressampling
out_ressampling = os.path.join(
working_dir, "IPEDGRealL2_{}.tif".format(res_str))
#threshold to 0.001 has the same result as multiplynig by 1000 and threshold to 1 after
app = resample(self._edgsubmask,
self._dem.ALTList[r],
out_ressampling + ":uint8",
OtbResampleType.LINEAR,
threshold=0.001,
write_output=False)
self._edg_pipeline.add_otb_app(app)
self._l2edgmasklist.append(app.getoutput().get("out"))
def generate_l2_sat_images(self, l_ListOfL2Resolution, working_dir):
for l1res in range(len(l_ListOfL2Resolution)):
curL1Res = l_ListOfL2Resolution[l1res]
l_ListOfL2BandCode = self._plugin.BandsDefinitions.get_list_of_l2_band_code(
curL1Res)
dtm = self._dem.ALTList[0]
tmp_constant_sat_filename = os.path.join(
working_dir, "SAT_constant_image_{}.tif".format(l1res))
app_constant_sat_app = constant_image(dtm,
0,
tmp_constant_sat_filename,
write_output=False)
self._sat_pipeline.add_otb_app(app_constant_sat_app)
# ******************************************************************************************************
# L2 SAT image pipeline connection
# ******************************************************************************************************
for band in l_ListOfL2BandCode:
self._satmasklist.append(
app_constant_sat_app.getoutput().get("out"))
out_sat_concatenate = os.path.join(
working_dir, "SATVectorImageList{}.tif".format(l1res))
param_concatenate_sat = {
"il": self._satmasklist,
"out": out_sat_concatenate + ":uint8"
}
concatenate_sat_app = OtbAppHandler("ConcatenateDoubleImages",
param_concatenate_sat,
write_output=False)
self._sat_pipeline.add_otb_app(concatenate_sat_app)
# L2SAT
self._l2satmasklist.append(
concatenate_sat_app.getoutput().get("out"))
def generate_sat_sub_image(self, l2comm, working):
# Sat are not provided on landsat 8 thus no need to resample or threshold anything, just create constant
# images at coarse resolution
dtm_coarse = self._dem.ALC
l_ListOfCoarseBandCode = self._plugin.BandsDefinitions.get_list_of_band_id_in_l2_coarse(
)
tmp_constant_sat_filename = os.path.join(working,
"SATSub_constant_image.tif")
app_constant_sat_app = constant_image(dtm_coarse,
0,
tmp_constant_sat_filename +
":uint8",
write_output=False)
self._sat_sub_pipeline.add_otb_app(app_constant_sat_app)
# ******************************************************************************************************
# Sub SAT image pipeline connection
# ******************************************************************************************************
for band in l_ListOfCoarseBandCode:
self._satsublist.append(
app_constant_sat_app.getoutput().get("out"))
# end band loop
#concatenate const image to have the vector image with coarse nb bands
sat_sub_image = os.path.join(working, "sat_sub.tif")
param_concatenate = {
"il": self._satsublist,
"out": sat_sub_image + ":uint8"
}
app_concatenate = OtbAppHandler("ConcatenateMaskImages",
param_concatenate)
self._subsatimage = app_concatenate.getoutput().get("out")
self._sat_sub_pipeline.add_otb_app(app_concatenate)
self._sat_sub_pipeline.free_otb_app()
def generate_cla_image(self, working_dir):
LOGGER.debug(
"Cloud Altitude (CLA) image doesn't exist. Replaced by constant nodata image. !"
)
cla_filename = os.path.join(working_dir, "cla_constant_sub.tif")
cla_image = constant_image(self._dem.ALC,
self._reall1nodata,
cla_filename,
write_output=True).getoutput()["out"]
self._claimage = cla_image
def generate_sol1_image(self, sol_h1, working_dir):
const_pi_18 = math.pi / 180
sol_filename = os.path.join(working_dir, "sol1.tif")
mean_solar = self._header_handler.get_mean_solar_angles()
l_sol1 = sol_h1 * math.tan(const_pi_18 * mean_solar[0]) * math.sin(
const_pi_18 * mean_solar[1])
l_sol2 = sol_h1 * math.tan(const_pi_18 * mean_solar[0]) * math.cos(
const_pi_18 * mean_solar[1])
cla_app_1 = constant_image(self._dem.ALC,
l_sol1,
sol_filename,
write_output=False)
cla_app_2 = constant_image(self._dem.ALC,
l_sol2,
sol_filename,
write_output=False)
out_concatenate = os.path.join(working_dir, "sol1_concat.tif")
param_concatenate = {
"il": [
cla_app_1.getoutput().get("out"),
cla_app_2.getoutput().get("out")
],
"out":
out_concatenate
}
app = OtbAppHandler("ConcatenateDoubleImages", param_concatenate)
self._sol1image = app.getoutput().get("out")
self._sol1_pipeline.add_otb_app(app)
def generate_vie_image(self, view_href, working_dir):
const_pi_18 = math.pi / 180
vie_filename = os.path.join(working_dir, "vie.tif")
mean_solar = self._header_handler.get_mean_solar_angles()
l_vie1 = view_href * math.tan(const_pi_18 * mean_solar[0]) * math.sin(
const_pi_18 * mean_solar[1])
l_vie2 = view_href * math.tan(const_pi_18 * mean_solar[0]) * math.cos(
const_pi_18 * mean_solar[1])
app_1 = constant_image(self._dem.ALC,
l_vie1,
vie_filename,
write_output=False)
app_2 = constant_image(self._dem.ALC,
l_vie2,
vie_filename,
write_output=False)
out_concatenate = os.path.join(working_dir, "vie_concat.tif")
param_concatenate = {
"il": [app_1.getoutput().get("out"),
app_2.getoutput().get("out")],
"out": out_concatenate
}
app = OtbAppHandler("ConcatenateDoubleImages", param_concatenate)
self._vieimage.append(app.getoutput().get("out"))
self._vie_pipeline.add_otb_app(app)
def read(self, product_info, app_handler, l2comm, dem, pReadL1Mode):
product_filename = product_info.HeaderFilename
LOGGER.debug("Start Landsat8 L1 ImageFileReader with the filename: " +
product_filename)
self._plugin.initialize(app_handler)
self._header_handler = product_info.HeaderHandler
self._GIPPL2COMMHandler = l2comm
self._dem = dem
working_dir = app_handler.get_directory_manager(
).get_temporary_directory("L1Read_", do_always_remove=True)
self.ReadL1Mode = pReadL1Mode
l_BandsDefinitions = self._plugin.BandsDefinitions
l_factor = xml_tools.as_float_list(l2comm.get_value("CalAdjustFactor"))
l_ListOfL1Resolution = l_BandsDefinitions.ListOfL1Resolution # ListOfStrings
l_ListOfL2Resolution = l_BandsDefinitions.ListOfL2Resolution # ListOfStrings
l_SolZenith = self._header_handler.get_sol_zenith()
# READ THE Inputs
# *********************************************************************************************************
# QB Reader connection
# *********************************************************************************************************
qb_filename = self._header_handler.get_qbi_image_filename()
self.generate_qb_mask(qb_filename, working_dir)
# *********************************************************************************************************
# TOA Reader connection
# *********************************************************************************************************
self.generate_l1_toa_image(l2comm, l_factor, l_SolZenith,
l_ListOfL1Resolution, working_dir)
# *********************************************************************************************************
# PIX image pipeline connection
# *********************************************************************************************************
self.generate_pix_mask(working_dir)
# *********************************************************************************************************
# START READ L1 for ALGORITHMS
# *********************************************************************************************************
if pReadL1Mode == ReadL1Mode.READ_L1_MODE_FOR_ALGORITHMS:
# Get information of areas (footprint) of the product (Origin, Spacing and Size for L2 and
# L2Coarse resolution)
l_L2Dems = dem.ALTList
l_CoarseDem = dem.ALC
# ************************************************************************************************
# TOA image pipeline connection
# ************************************************************************************************
self.generate_l2_toa_images(l_ListOfL2Resolution, working_dir)
# ************************************************************************************************
# L2SAT image pipeline connection
# ************************************************************************************************
self.generate_l2_sat_images(l_ListOfL2Resolution, working_dir)
# ************************************************************************************************
# L2 EDG image pipeline connection
# ************************************************************************************************
self.generate_edg_images(working_dir)
# ************************************************************************************************
# TOA Sub image pipeline connection
# ************************************************************************************************
self.generate_toa_sub_image(working_dir)
# *********************************.**************************************************************
# SAT Sub image pipeline connection
# ************************************************************************************************
self.generate_sat_sub_image(l2comm, working_dir)
# ************************************************************************************************
# CLA image pipeline connectio
# ************************************************************************************************
self.generate_cla_image(working_dir)
# ************************************************************************************************
# SOL1 image pipeline connectio
# ************************************************************************************************
sol_h1 = self._plugin.ConfigUserCamera.get_Algorithms(
).get_GRID_Reference_Altitudes().get_SOLH1()
self.generate_sol1_image(sol_h1, working_dir)
# *************************************************************************************************
# VIE image pipeline connection
# *************************************************************************************************
vie_href = self._plugin.ConfigUserCamera.get_Algorithms(
).get_GRID_Reference_Altitudes().get_VIEHRef()
self.generate_vie_image(vie_href, working_dir)
# Fill the datas
self.dict_of_vals["IPEDGSubOutput"] = self._edgsubmask
self.dict_of_vals["L2TOAImageList"] = self._l2toaimagelist
self.dict_of_vals["L2SATImageList"] = self._l2satmasklist
self.dict_of_vals["L1PIXImageList"] = self._l2piximagelist
self.dict_of_vals["L2PIXImageList"] = self._l2piximagelist
self.dict_of_vals["L2EDGOutputList"] = self._l2edgmasklist
self.dict_of_vals["IPTOASubOutput"] = self._subtoaimage
self.dict_of_vals["IPSATSubOutput"] = self._subsatimage
self.dict_of_vals["CLAImage"] = self._claimage
self.dict_of_vals["SOL1Image"] = self._sol1image
self.dict_of_vals["ViewingZenithMeanMap"] = self._meanZenithMap
self.dict_of_vals["ViewingAzimuthMeanMap"] = self._meanAzimuthMap
if len(self._vieimage) > 2:
l_DTMBandCode = l2comm.get_value("DTMViewingDirectionBandCode")
l_DTMBandIdx = l_BandsDefinitions.get_band_id_in_l2_coarse(
l_DTMBandCode)
LOGGER.info("DTMBandCode= " + l_DTMBandCode)
self.dict_of_vals["DTMVIEImage"] = self._vieimage[l_DTMBandIdx]
else:
self.dict_of_vals["DTMVIEImage"] = self._vieimage[0]
if len(self._vieimage) > 2:
l_CLDBandCode = l2comm.get_value("CLDViewingDirectionBandCode")
l_CLDBandIdx = self._plugin.BandsDefinitions.get_band_id_in_l2_coarse(
l_CLDBandCode)
self.dict_of_vals["ShadowVIEImage"] = self._vieimage[
l_CLDBandIdx]
elif len(self._vieimage) > 1:
self.dict_of_vals["ShadowVIEImage"] = self._vieimage[1]
else:
self.dict_of_vals["ShadowVIEImage"] = self._vieimage[0]
if self._plugin.CirrusMasking:
l_CirrusBandCode = l2comm.get_value("CirrusBandCode")
l_CirrusRes = self._plugin.BandsDefinitions.get_l1_resolution_for_band_code(
l_CirrusBandCode)
l_CirrusBandIdx = self._plugin.BandsDefinitions.get_band_id_in_l1(
l_CirrusBandCode)
tmp_toa_roi = os.path.join(working_dir, "L1TOACirrusImage.tif")
l2toa_roi_app = extract_roi(self._l1toaimagelist[0],
[l_CirrusBandIdx],
tmp_toa_roi,
write_output=False)
self._l2toa_pipeline.add_otb_app(l2toa_roi_app)
self.dict_of_vals[
"L1TOACirrusImage"] = l2toa_roi_app.getoutput().get("out")
self.dict_of_vals["L1TOAImageList"] = self._l1toaimagelist
class MajaMaskResampling(MajaModule):
"""
classdocs
"""
NAME = "CloudMaskResampling"
def __init__(self):
"""
Constructor
"""
super(MajaMaskResampling, self).__init__()
self.in_keys_to_check = ["AppHandler", "Plugin", "DEM"]
self.out_keys_to_check = []
self.out_keys_provided = ["L2CLDList"]
self._apps = OtbPipelineManager()
def cleanup(self):
LOGGER.debug("Cleanup of module : "+MajaMaskResampling.NAME)
self._apps.free_otb_app()
def run(self, dict_of_input, dict_of_output):
LOGGER.info("Cloud mask resampling start")
p_working = dict_of_input.get("AppHandler").get_directory_manager().get_temporary_directory("CloudMaskResampling_",
do_always_remove=True)
bands_definition = dict_of_input.get("Plugin").BandsDefinitions
do_write = is_croco_on("cloudmaskresampling")
l2_cld_list = []
for p_res in range(0, len(bands_definition.ListOfL2Resolution)):
l_res = bands_definition.ListOfL2Resolution[p_res]
# ---------- Resample All to resolution ---------------
cld_all_resampled = os.path.join(p_working, "cloud_all_" + l_res + ".tif")
cld_all_app = resample(dict_of_output[CLOUD_MASK_ALL], dict_of_input.get("DEM").ALTList[p_res],
cld_all_resampled, OtbResampleType.LINEAR,threshold=0.25,write_output=do_write)
self._apps.add_otb_app(cld_all_app)
dict_of_output[CLOUD_MASK_ALL + "_" + l_res] = cld_all_app.getoutput().get("out")
# ---------- Resample All cloud to resolution ---------------
cld_allclouds_resampled = os.path.join(p_working, "cloud_allclouds_" + l_res + ".tif")
cld_allclouds_app = resample(dict_of_output[CLOUD_MASK_ALL_CLOUDS], dict_of_input.get("DEM").ALTList[p_res],
cld_allclouds_resampled, OtbResampleType.LINEAR,threshold=0.25,write_output=do_write)
self._apps.add_otb_app(cld_allclouds_app)
dict_of_output[CLOUD_MASK_ALL_CLOUDS + "_" + l_res] = cld_allclouds_app.getoutput().get("out")
# ---------- Resample shadow to resolution ---------------
cld_shadows_resampled = os.path.join(p_working, "cloud_shadows_" + l_res + ".tif")
cld_shadows_app = resample(dict_of_output[CLOUD_MASK_SHADOWS], dict_of_input.get("DEM").ALTList[p_res],
cld_shadows_resampled, OtbResampleType.LINEAR,threshold=0.25,write_output=do_write)
self._apps.add_otb_app(cld_shadows_app)
dict_of_output[CLOUD_MASK_SHADOWS + "_" + l_res] = cld_shadows_app.getoutput().get("out")
# ---------- Resample shadvar to resolution ---------------
cld_shadvar_resampled = os.path.join(p_working, "cloud_shadvar_" + l_res + ".tif")
cld_shadvar_app = resample(dict_of_output[CLOUD_MASK_SHADVAR], dict_of_input.get("DEM").ALTList[p_res],
cld_shadvar_resampled, OtbResampleType.LINEAR,threshold=0.25,write_output=do_write)
self._apps.add_otb_app(cld_shadvar_app)
dict_of_output[CLOUD_MASK_SHADVAR + "_" + l_res] = cld_shadvar_app.getoutput().get("out")
# ---------- Resample Refl cloud to resolution ---------------
cld_refl_resampled = os.path.join(p_working, "cloud_refl_" + l_res + ".tif")
cld_refl_app = resample(dict_of_output[CLOUD_MASK_REFL], dict_of_input.get("DEM").ALTList[p_res], cld_refl_resampled,
OtbResampleType.LINEAR,threshold=0.25,write_output=do_write)
self._apps.add_otb_app(cld_refl_app)
dict_of_output[CLOUD_MASK_REFL + "_" + l_res] = cld_refl_app.getoutput().get("out")
# ---------- Resample ReflVar cloud to resolution ---------------
cld_reflvar_resampled = os.path.join(p_working, "cloud_reflvar_" + l_res + ".tif")
cld_reflvar_app = resample(dict_of_output[CLOUD_MASK_REFL_VAR], dict_of_input.get("DEM").ALTList[p_res],
cld_reflvar_resampled,
OtbResampleType.LINEAR,threshold=0.25,write_output=do_write)
self._apps.add_otb_app(cld_reflvar_app)
dict_of_output[CLOUD_MASK_REFL_VAR + "_" + l_res] = cld_reflvar_app.getoutput().get("out")
# ---------- Resample Extension cloud to resolution ---------------
cld_ext_resampled = os.path.join(p_working, "cloud_ext_" + l_res + ".tif")
cld_ext_app = resample(dict_of_output[CLOUD_MASK_EXTENSION], dict_of_input.get("DEM").ALTList[p_res], cld_ext_resampled,
OtbResampleType.LINEAR,threshold=0.25,write_output=do_write)
self._apps.add_otb_app(cld_ext_app)
dict_of_output[CLOUD_MASK_EXTENSION + "_" + l_res] = cld_ext_app.getoutput().get("out")
# ---------- Resample Alt to resolution ---------------
cld_alt_resampled = os.path.join(p_working, "cloud_alt_" + l_res + ".tif")
cld_alt_app = resample(dict_of_output[CLOUD_MASK_ALT], dict_of_input.get("DEM").ALTList[p_res], cld_alt_resampled,
OtbResampleType.LINEAR,threshold=0.25,write_output=do_write)
self._apps.add_otb_app(cld_alt_app)
dict_of_output[CLOUD_MASK_ALT + "_" + l_res] = cld_alt_app.getoutput().get("out")
# ---------- Resample Cirrus cloud to resolution ---------------
cld_cirrus_resampled = os.path.join(p_working, "cloud_cirrus_" + l_res + ".tif")
cld_cirrus_app = resample(dict_of_output[CLOUD_MASK_CIRRUS], dict_of_input.get("DEM").ALTList[p_res], cld_cirrus_resampled,
OtbResampleType.LINEAR,threshold=0.25,write_output=do_write)
self._apps.add_otb_app(cld_cirrus_app)
dict_of_output[CLOUD_MASK_CIRRUS + "_" + l_res] = cld_cirrus_app.getoutput().get("out")
# APRES MEMORY IN CHAIN CORE ALGORITHMS : V 4-1-0
# Bit 1 - Cloud_Mask.all : summary Logical or of All cloud, extension and shadow masks
# Bit 2 - Cloud_Mask.all clouds: Logical or of All cloud masks and extension
# Bit 3 - Cloud_Mask.shadows : shadows mask from clouds within image
# Bit 4 - Cloud_Mask.shadvar: shadows mask from clouds outside image bit 1 : Cloud_Mask.all
# Bit 5 - Cloud_Mask.refl : reflectance threshold
# Bit 6 - Cloud_Mask.refl_var : reflectance variation threshold
# Bit 7 - Cloud_Mask.extension : extension of the cloud mask
# Bit 8 - Cloud_Mask.alt : stereoscopic mask => VENUS et vide pour les autres
# Bit 9 - Cirrus : Pour L8 et S2 et vide pour les autres
# FORMAT DISK
# Bit 1 - Cloud_Mask.all : summary Logical or of All cloud and shadow masks
# Bit 2 - Cloud_Mask.all clouds: Logical or of All cloud masks
# Bit 3 - Cloud_Mask.shadows : shadows mask from clouds within image
# Bit 4 - Cloud_Mask.shadvar: shadows mask from clouds outside image bit 1 : Cloud_Mask.all
# Bit 5 - Cloud_Mask.refl : reflectance threshold
# Bit 6 - Cloud_Mask.refl_var : reflectance variation threshold
# Bit 7 - Cloud_Mask.extension : extension of the cloud mask
# Bit 8 - VENUS : Cloud_Mask.alt : stereoscopic mask
# Bit 8 - L8 et S2 : Bit 9 - Cirrus
# Bit 8 - Vide pour les autres
cld_list = []
cld_list.append(dict_of_output[CLOUD_MASK_ALL + "_" + l_res])
cld_list.append(dict_of_output[CLOUD_MASK_ALL_CLOUDS + "_" + l_res])
cld_list.append(dict_of_output[CLOUD_MASK_SHADOWS + "_" + l_res])
cld_list.append(dict_of_output[CLOUD_MASK_SHADVAR + "_" + l_res])
cld_list.append(dict_of_output[CLOUD_MASK_REFL + "_" + l_res])
cld_list.append(dict_of_output[CLOUD_MASK_REFL_VAR + "_" + l_res])
cld_list.append(dict_of_output[CLOUD_MASK_EXTENSION + "_" + l_res])
cld_list.append(dict_of_output[CLOUD_MASK_ALT + "_" + l_res])
cld_list.append(dict_of_output[CLOUD_MASK_CIRRUS + "_" + l_res])
l2_cld_list.append(cld_list)
dict_of_output["L2CLDList"] = l2_cld_list
class MajaEnvCorr(MajaModule):
"""
classdocs
"""
NAME = "EnvironmentCorrection"
def __init__(self):
"""
Constructor
"""
super(MajaEnvCorr, self).__init__()
self.in_keys_to_check = ["Params.Caching", "AppHandler", "Plugin", "L1Reader","L2COMM", "DEM",
"L2DIRT", "L2DIFT", "L2ALBD", "Params.RealL2NoData"]
self.out_keys_to_check = ["TOC_sub", "AOT_Sub"]
self.out_keys_provided = ["SRE_List"]
self._l2_pipeline = OtbPipelineManager()
def __del__(self):
self._l2_pipeline.free_otb_app()
def run(self, dict_of_input, dict_of_output):
LOGGER.info("Environment Correction start")
env_working = dict_of_input.get("AppHandler").get_directory_manager().get_temporary_directory("EnvoCorrProc_",
do_always_remove=True)
l_envcorradius = dict_of_input.get("L2COMM").get_value_f("EnvCorrRadius")
l_envcorsize = (2 * l_envcorradius + 1) * (2 * l_envcorradius + 1)
caching = dict_of_input.get("Params").get("Caching")
# Compute rho env sub
rhoenv_sub_image = os.path.join(env_working, "rhoenv_sub.tif")
param_computerho = {"toc": dict_of_output["TOC_sub"],
"edg": dict_of_input.get("L1Reader").get_value("IPEDGSubOutput"),
"radius": l_envcorradius,
"filtercoeff": 1.0 / l_envcorsize,
"rhoenv": rhoenv_sub_image}
computerho_app = OtbAppHandler("ComputeRhoEnv", param_computerho, write_output=True)
# Interpolate for env
tdif_filename = os.path.join(env_working, "tdif_sub.tif")
tdir_filename = os.path.join(env_working, "tdir_sub.tif")
albd_filename = os.path.join(env_working, "albd_sub.tif")
param_interpolate = {"luttdir": dict_of_input.get("L2DIRT"),
"luttdif": dict_of_input.get("L2DIFT"),
"lutalbd": dict_of_input.get("L2ALBD"),
"aot": dict_of_output["AOT_Sub"],
"dtm": dict_of_input.get("DEM").ALC,
"edg": dict_of_input.get("L1Reader").get_value("IPEDGSubOutput"),
"thetav": dict_of_input.get(
"L1Info").ListOfViewingZenithAnglesPerBandAtL2CoarseResolution,
"tdir": tdir_filename,
"tdif": tdif_filename,
"albd": albd_filename
}
interpolate_app = OtbAppHandler("InterpolateForEnvCorr", param_interpolate, write_output=True)
tdif_image = interpolate_app.getoutput()["tdif"]
tdir_image = interpolate_app.getoutput()["tdir"]
albd_image = interpolate_app.getoutput()["albd"]
# Compute env corr for each l2 resolution
bands_definition = dict_of_input.get("Plugin").BandsDefinitions
l_nbRes = len(bands_definition.ListOfL2Resolution)
sre_list = []
for r in range(0, l_nbRes):
l_res = bands_definition.ListOfL2Resolution[r]
l_l2bandcodes = bands_definition.get_list_of_l2_band_code(l_res)
l_l2bandidx = [bands_definition.get_band_id_in_l2_coarse(b) for b in l_l2bandcodes]
tdifl2_filename = os.path.join(env_working, "tdif_" + l_res + ".tif")
tdirl2_filename = os.path.join(env_working, "tdir_" + l_res + ".tif")
albdl2_filename = os.path.join(env_working, "albd_" + l_res + ".tif")
rhoenvl2_filename= os.path.join(env_working, "rhoenv_extract_" + l_res + ".tif")
# Extract tdif
tdifl2_image_app = extract_roi(tdif_image, l_l2bandidx, tdifl2_filename, write_output=False)
self._l2_pipeline.add_otb_app(tdifl2_image_app)
# Extract tdir
tdirl2_image_app = extract_roi(tdir_image, l_l2bandidx, tdirl2_filename, write_output=False)
self._l2_pipeline.add_otb_app(tdirl2_image_app)
# Extract albd
albdl2_image_app = extract_roi(albd_image, l_l2bandidx, albdl2_filename, write_output=False)
self._l2_pipeline.add_otb_app(albdl2_image_app)
# Extract rhoenv_sub
rhoenvl2_image_app = extract_roi(computerho_app.getoutput().get("rhoenv"), l_l2bandidx, rhoenvl2_filename, write_output=False)
self._l2_pipeline.add_otb_app(rhoenvl2_image_app)
rhoenv_image = os.path.join(env_working, "rhoenv_" + l_res + ".tif")
sre_image = os.path.join(env_working, "sre_" + l_res + ".tif")
# Compute env correction
param_envcorr = {"tdir": tdirl2_image_app.getoutput()["out"],
"tdif": tdifl2_image_app.getoutput()["out"],
"albd": albdl2_image_app.getoutput()["out"],
"rhoenvsub": rhoenvl2_image_app.getoutput()["out"],
"nodata": dict_of_input.get("Params").get("RealL2NoData"),
"toc": dict_of_output["TOC_" + l_res],
"edg": dict_of_input.get("L1Reader").get_value("L2EDGOutputList")[r],
"sre": sre_image,
"rhoenv": rhoenv_image
}
envcorr_app = OtbAppHandler("EnvCorrection", param_envcorr,write_output=False)
self._l2_pipeline.add_otb_app(envcorr_app)
if is_croco_on("envcorrection"):
write_images([envcorr_app.getoutput().get("sre"), envcorr_app.getoutput().get("rhoenv")],
[sre_image, rhoenv_image])
dict_of_output["SRE_" + l_res] = sre_image
dict_of_output["RhoEnv_" + l_res] = rhoenv_image
sre_list.append(sre_image)
else:
dict_of_output["SRE_" + l_res] = envcorr_app.getoutput().get("sre")
dict_of_output["RhoEnv_" + l_res] = envcorr_app.getoutput().get("rhoenv")
sre_list.append(envcorr_app.getoutput().get("sre"))
dict_of_output["SRE_List"] = sre_list
def generate_edg_images_from_toa(self, listOfTOAImageFileNames, working):
"""
:param listOfTOAImageFileNames: list
:param working: path
:return:
"""
# Get the number of band with the number of TOA image files set in the input product directory
l_NbBand = len(listOfTOAImageFileNames) # int
# *******************************************************************************************************
# L2SubEDG pipeline connection
# *******************************************************************************************************
LOGGER.debug("Start IPEDGSub")
m_OneBandFilterList = []
m_ResamplingList = []
m_OrFilterList = []
tmp_edg_pipe = OtbPipelineManager()
for i in range(2):
toaFilename = listOfTOAImageFileNames[i]
LOGGER.debug(("toaFilename : ", toaFilename))
out_oneBandValue = os.path.join(working,
"OneBandValue" + str(i) + ".tif")
band_math_OneBand = one_band_equal_value(
self._toa_scalar_list[i],
output_image=out_oneBandValue + ":uint8",
threshold=0.0,
write_output=False)
tmp_edg_pipe.add_otb_app(band_math_OneBand)
m_OneBandFilterList.append(band_math_OneBand.getoutput()["out"])
out_resample = os.path.join(working,
"mask_resample" + str(i) + ".tif")
resample_mask = resample(band_math_OneBand.getoutput()["out"],
self._dem.ALC,
out_resample + ":uint8",
OtbResampleType.LINEAR_WITH_RADIUS,
threshold=0.5,
write_output=False)
tmp_edg_pipe.add_otb_app(resample_mask)
m_ResamplingList.append(resample_mask.getoutput()["out"])
for i in range(l_NbBand - 2):
out_oneBandValue = os.path.join(
working, "OneBandValue" + str(i + 2) + ".tif")
band_math_OneBand = one_band_equal_value(
self._toa_scalar_list[i + 2],
output_image=out_oneBandValue + ":uint8",
threshold=0.0,
write_output=False)
tmp_edg_pipe.add_otb_app(band_math_OneBand)
m_OneBandFilterList.append(band_math_OneBand.getoutput()["out"])
out_resample = os.path.join(working,
"mask_resample" + str(i + 2) + ".tif")
resample_mask = resample(band_math_OneBand.getoutput()["out"],
self._dem.ALC,
out_resample + ":uint8",
OtbResampleType.LINEAR_WITH_RADIUS,
threshold=0.5,
write_output=False)
tmp_edg_pipe.add_otb_app(resample_mask)
m_ResamplingList.append(resample_mask.getoutput()["out"])
out_concat = os.path.join(working, "ConcatSubEdgOneBand.tif")
param_oneband_concat = {
"il": m_ResamplingList,
"out": out_concat + ":uint8"
}
qoth_concat_app = OtbAppHandler("ConcatenateMaskImages",
param_oneband_concat,
write_output=False)
tmp_edg_pipe.add_otb_app(qoth_concat_app)
out_or0 = os.path.join(working, "MaskOrMask_0.tif")
band_math_or_b1 = one_band_equal_value(
qoth_concat_app.getoutput().get("out"),
output_image=out_or0 + ":uint8",
threshold=1)
self._edgsubmask = band_math_or_b1.getoutput().get("out")
tmp_edg_pipe.add_otb_app(band_math_or_b1)
tmp_edg_pipe.free_otb_app()
LOGGER.debug("End IPEDGSub.")
# *******************************************************************************************************
# L2EDG pipeline connection
# *******************************************************************************************************
LOGGER.debug("Start L2EDG ...")
l_BandsDefinitions = self._plugin.BandsDefinitions
LOGGER.debug("BANDS DEFINITION")
LOGGER.debug(l_BandsDefinitions)
l_ListOfL2Resolution = l_BandsDefinitions.ListOfL2Resolution # ListOfStrings
# At L2 resolution
l_NbL2Res = len(l_ListOfL2Resolution)
for r in range(l_NbL2Res):
res_str = l_ListOfL2Resolution[r]
# Set the threshold to 0.0.001 so that all pixel above 1/1000 to edge pixels
# to identify the pixel contaminated by an edge pixel after resampling
# ExpandFilterPointer => PadAndResampleImageFilter => app ressampling
out_ressampling = os.path.join(
working, "IPEDGRealL2_{}.tif".format(res_str))
l2edg_resamp_app = resample(
self._edgsubmask,
self._dem.ALTList[r],
out_ressampling + ":uint8",
OtbResampleType.LINEAR,
threshold=0.0,
write_output=(False
or is_croco_on("sentinel2.l1reader.l2edg")))
self._l2edg_pipeline.add_otb_app(l2edg_resamp_app)
self._l2edgmasklist.append(l2edg_resamp_app.getoutput().get("out"))
LOGGER.debug("End L2EDG ...")
class MajaWaterVaporPostProcessing(MajaModule):
"""
classdocs
"""
NAME = "WaterVaporPostProcessing"
def __init__(self):
"""
Constructor
"""
super(MajaWaterVaporPostProcessing, self).__init__()
self.in_keys_to_check = [
"Params.Caching", "AppHandler", "Plugin", "L1Reader", "L2Reader",
"L2COMM", "DEM"
]
self.out_keys_to_check = ["RayleighIPTOCR", "dtm_shd", "SunglintFlag"]
self.out_keys_provided = ["L2VAPList", "L2VAPMaskList"]
self._l2_pipeline = OtbPipelineManager()
def run(self, dict_of_input, dict_of_output):
LOGGER.info("Water vapor post processing Correction start")
caching = dict_of_input.get("Params").get("Caching")
wvpp_working = dict_of_input.get(
"AppHandler").get_directory_manager().get_temporary_directory(
"WaterVaporPPProc_", do_always_remove=True)
vap_default = float(
dict_of_input.get("L2COMM").get_value("WaterAmountDefaultValue"))
bands_definition = dict_of_input.get("Plugin").BandsDefinitions
l_nbRes = len(bands_definition.ListOfL2Resolution)
# Does the user choose to compute the VAP image ? (GIP_L2COMM_UseDefaultConstantWaterAmount)
l_UseDefaultConstantWaterAmount = False
if not dict_of_input.get("Plugin").WaterVapourDetermination or as_bool(
dict_of_input.get("L2COMM").get_value(
"GIP_L2COMM_UseDefaultConstantWaterAmount")):
l_UseDefaultConstantWaterAmount = True
vap_list = []
vapmask_list = []
if l_UseDefaultConstantWaterAmount:
for r in range(0, l_nbRes):
l_res = bands_definition.ListOfL2Resolution[r]
vap_image_filename = os.path.join(wvpp_working,
"vap_" + l_res + ".tif")
vap_mask_filename = os.path.join(
wvpp_working, "vapmask_" + l_res + ".tif:uint8")
vap_image = constant_image(dict_of_input.get("DEM").ALTList[r],
vap_default,
vap_image_filename,
write_output=False)
self._l2_pipeline.add_otb_app(vap_image)
dict_of_output["VAP_" + l_res] = vap_image.getoutput()["out"]
vap_mask = constant_image(dict_of_input.get("DEM").ALTList[r],
0,
vap_mask_filename,
write_output=False)
self._l2_pipeline.add_otb_app(vap_mask)
dict_of_output["VAPMASK_" +
l_res] = vap_mask.getoutput()["out"]
vap_list.append(dict_of_output["VAP_" + l_res])
vapmask_list.append(dict_of_output["VAPMASK_" + l_res])
else:
l_refbandcode = dict_of_input.get("L2COMM").get_value(
"WaterVaporReferenceBandCode")
l_bandcode = dict_of_input.get("L2COMM").get_value(
"WaterVaporBandCode")
vap_subimage_filename = os.path.join(wvpp_working, "vap_sub.tif")
vap_submask_filename = os.path.join(wvpp_working,
"vapmask_sub.tif")
param_watervpp = {
"tocr":
dict_of_output["RayleighIPTOCR"],
"vapin":
dict_of_output["VAP_Sub"],
"cldsum":
dict_of_output[constants.CLOUD_MASK_ALL],
"defaultwatervapor":
vap_default,
"threshold":
dict_of_input.get("L2COMM").get_value_f(
"WaterVaporReflectanceThreshold"),
"thresholdref":
dict_of_input.get("L2COMM").get_value_f(
"WaterVaporReflectanceThresholdRef"),
"referencebandcode":
bands_definition.get_band_id_in_l2_coarse(l_refbandcode),
"bandcode":
bands_definition.get_band_id_in_l2_coarse(l_bandcode),
"nodata":
dict_of_input.get("L2COMM").get_value_f("VAPNodataValue"),
"sevalidradius":
dict_of_input.get("L2COMM").get_value_i(
"WaterVaporSEValidRadius"),
"initwindowradius":
dict_of_input.get("L2COMM").get_value_i(
"WaterVaporInitWindowRadius"),
"maxwindowradius":
dict_of_input.get("L2COMM").get_value_i(
"WaterVaporMaxWindowRadius"),
"mask":
vap_submask_filename + ":uint8",
"vap":
vap_subimage_filename
}
watervpp_app = OtbAppHandler("WaterVaporPostPro",
param_watervpp,
write_output=False)
self._l2_pipeline.add_otb_app(watervpp_app)
for r in range(0, l_nbRes):
l_res = bands_definition.ListOfL2Resolution[r]
vap_image_filename = os.path.join(wvpp_working,
"vap_" + l_res + ".tif")
vap_mask_filename = os.path.join(wvpp_working,
"vapmask_" + l_res + ".tif")
param_subresampling = {
"dtm": dict_of_input.get("DEM").ALTList[r],
"im": watervpp_app.getoutput()["vap"],
"interp": "linear",
"out": vap_image_filename
}
subresampling_app = OtbAppHandler("Resampling",
param_subresampling,
write_output=False)
self._l2_pipeline.add_otb_app(subresampling_app)
param_maskresampling = {
"dtm": dict_of_input.get("DEM").ALTList[r],
"im": watervpp_app.getoutput()["mask"],
"interp": "linear",
"out": vap_mask_filename + ":uint8",
"threshold": 0.25
}
maskresampling_app = OtbAppHandler("Resampling",
param_maskresampling,
write_output=False)
self._l2_pipeline.add_otb_app(maskresampling_app)
if is_croco_on("watervaporpostprocessing"):
write_images([
subresampling_app.getoutput()["out"],
maskresampling_app.getoutput()["out"]
], [vap_image_filename, vap_mask_filename])
dict_of_output["VAP_" + l_res] = vap_image_filename
dict_of_output["VAPMASK_" + l_res] = vap_mask_filename
else:
dict_of_output[
"VAP_" + l_res] = subresampling_app.getoutput()["out"]
dict_of_output[
"VAPMASK_" +
l_res] = maskresampling_app.getoutput()["out"]
vap_list.append(dict_of_output["VAP_" + l_res])
vapmask_list.append(dict_of_output["VAPMASK_" + l_res])
dict_of_output["L2VAPList"] = vap_list
dict_of_output["L2VAPMaskList"] = vapmask_list
def write_public_images(
self,
p_L2ImageFilenamesProvider,
p_ReflectanceQuantificationValue,
p_AOTQuantificationValue,
p_AOTNodataValue,
p_VAPQuantificationValue,
p_VAPNodataValue,
p_CLDDataBandsSelected,
p_CLDCoreAlgorithmsMapBand,
p_WritePublicProduct,
p_EnvCorOption,
working_dir):
# == == == == == == == == == == == == == == == == == == == == == == == == == == == == == == == == == ==
# IF PUBLIC PART OF L2 PRODUCT IS WRITTEN
# == == == == == == == == == == == == == == == == == == == == == == == == == == == == == == == == == ==
if p_WritePublicProduct:
l_NumberOfResolutions = len(p_L2ImageFilenamesProvider.get_sre_headers())
LOGGER.debug("L2ImageFileWriterBase::Initialize Number of resolutions: " + str(l_NumberOfResolutions) + ".")
l_BandsDefinitions = self._plugin.BandsDefinitions
l_RedBandId = 0
l_GreenBandId = 0
l_BlueBandId = 0
resol_QLK = 0
l_Resolution = ""
l_BandsDefinitions = self._plugin.BandsDefinitions
l_RedBandId, l_BlueBandId, l_GreenBandId = l_BandsDefinitions.get_l2_information_for_quicklook_band_code(
self._quicklookredbandcode,
self._quicklookgreenbandcode,
self._quicklookbluebandcode,
)
# ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** *
# ** ** LOOP on RESOLUTION ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **
for resol in range(0, l_NumberOfResolutions):
l_StrResolution = l_BandsDefinitions.ListOfL2Resolution[resol]
# --------------------------------------------------------
# L2 area AreaType l_AreaToL2Resolution
l_AreaFile = self._sre_list[resol]
l_ListOfBand = l_BandsDefinitions.get_list_of_l2_band_code(l_StrResolution)
LOGGER.debug("L2ImageFileReader::Gen Public image file for the resolution " + l_StrResolution + ".")
# ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **
# Read the Coef apply for SRE and FRE images
LOGGER.info(
"SRE and FRE values multiply by the reflectance quantification value " +
str(p_ReflectanceQuantificationValue) +
".")
# ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **
# ** ** PUBLIC DATA ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **
#Store image pointer and filename
tmp_l2_filename_list = []
tmp_l2_image_list = []
tmp_l2_pipe = OtbPipelineManager()
# START WRITING SRE Image file DATA
# Caching the SRE image, before computing the QuickLook.
# Create the scalar image filter
sre_filename = p_L2ImageFilenamesProvider.get_sre_filenames()[
resol] + file_utils.get_extended_filename_write_image_file_standard()
param_scaled_sre = {
"im": self._sre_list[resol],
"coef": p_ReflectanceQuantificationValue,
"out": sre_filename+":int16"}
sre_scal_app = OtbAppHandler("MultiplyByScalar", param_scaled_sre,
write_output=is_croco_on("earthexplorer.l2writer.sre"))
tmp_l2_image_list.append(sre_scal_app.getoutput().get("out"))
tmp_l2_filename_list.append(sre_filename)
tmp_l2_pipe.add_otb_app(sre_scal_app)
#QuickLook stuff
if resol == resol_QLK :
tmp_sre_roi_red = os.path.join(working_dir, "tmp_sre_roi_red.tif")
tmp_sre_roi_red_app = extract_roi(self._sre_list[resol], [l_RedBandId],
tmp_sre_roi_red, write_output=is_croco_on("earthexplorer.l2writer.roi"))
tmp_l2_image_list.append(tmp_sre_roi_red_app.getoutput().get("out"))
tmp_l2_filename_list.append(tmp_sre_roi_red)
self._qckl_red_image = tmp_sre_roi_red
tmp_l2_pipe.add_otb_app(tmp_sre_roi_red_app)
tmp_sre_roi_green = os.path.join(working_dir, "tmp_sre_roi_green.tif")
tmp_sre_roi_green_app = extract_roi(self._sre_list[resol], [l_GreenBandId],
tmp_sre_roi_green, write_output=is_croco_on("earthexplorer.l2writer.roi"))
tmp_l2_image_list.append(tmp_sre_roi_green_app.getoutput().get("out"))
tmp_l2_filename_list.append(tmp_sre_roi_green)
self._qckl_green_image = tmp_sre_roi_green
tmp_l2_pipe.add_otb_app(tmp_sre_roi_green_app)
tmp_sre_roi_blue = os.path.join(working_dir, "tmp_sre_roi_blue.tif")
tmp_sre_roi_blue_app = extract_roi(self._sre_list[resol], [l_BlueBandId],
tmp_sre_roi_blue, write_output=is_croco_on("earthexplorer.l2writer.roi"))
tmp_l2_image_list.append(tmp_sre_roi_blue_app.getoutput().get("out"))
tmp_l2_filename_list.append(tmp_sre_roi_blue)
self._qckl_blue_image = tmp_sre_roi_blue
tmp_l2_pipe.add_otb_app(tmp_sre_roi_blue_app)
# ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **
# START WRITING FRE Image file DATA
fre_scal_app = None
if p_EnvCorOption:
fre_filename = p_L2ImageFilenamesProvider.get_fre_filenames()[
resol] + file_utils.get_extended_filename_write_image_file_standard()
param_scaled_fre = {
"im": self._fre_list[resol],
"coef": p_ReflectanceQuantificationValue,
"out": fre_filename+":int16"}
fre_scal_app = OtbAppHandler("MultiplyByScalar", param_scaled_fre,
write_output=is_croco_on("earthexplorer.l2writer.fre"))
#Write SRE and FRE simultaneously
tmp_l2_image_list.append(fre_scal_app.getoutput().get("out"))
tmp_l2_filename_list.append(fre_filename)
tmp_l2_pipe.add_otb_app(fre_scal_app)
# ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **
# START WRITING ATB Image file DATA
# Initialize the Scalar filter
# FA1424: Temporary Fix to address cosmetic aspects of FA1424
# VAPThreshold.SetInput(self.GetVAPImageList()[resol))
# VAPThreshold.SetOutsideValue(255. / p_VAPQuantificationValue)
# VAPThreshold.ThresholdAbove(255. / p_VAPQuantificationValue)
# VAPScalar.SetInput(VAPThreshold.GetOutput())
# VAPScalar.SetCoef(p_VAPQuantificationValue)
tmp_vap = os.path.join(working_dir, "tmp_vap_scaled_" + l_StrResolution + ".tif")
param_bandmath_vap = {
"il": [
self._l2vapimagelist[resol],
self._l2edgimagelist[resol]],
"exp": "(im2b1 == 1)?" +
str(p_VAPNodataValue) +
":" +
"rint(im1b1*" +
str(p_VAPQuantificationValue)+")",
"out": tmp_vap + ":uint8"}
vap_scal_app = OtbAppHandler("BandMathDouble", param_bandmath_vap, write_output=False)
tmp_aot = os.path.join(working_dir, "tmp_aot_scaled_" + l_StrResolution + ".tif")
param_bandmath_aot = {"il": [self._l2aotlist[resol], self._l2edgimagelist[resol]],
"exp": "(im2b1 == 1)?" + str(p_AOTNodataValue) + ":" + "rint(im1b1*" + str(
p_AOTQuantificationValue)+")",
"out": tmp_aot + ":uint8"
}
aot_scal_app = OtbAppHandler("BandMathDouble", param_bandmath_aot, write_output=False)
tmp_l2_pipe.add_otb_app(aot_scal_app)
atb_filename = p_L2ImageFilenamesProvider.get_atb_image_filename()[resol]
param_atb_concat = {"il": [vap_scal_app.getoutput().get("out"), aot_scal_app.getoutput().get("out")],
"out": atb_filename + ":uint8"
}
atb_concat_app = OtbAppHandler("ConcatenateMaskImages", param_atb_concat,
write_output=is_croco_on("earthexplorer.l2writer.atb"))
tmp_l2_image_list.append(atb_concat_app.getoutput().get("out"))
tmp_l2_filename_list.append(atb_filename)
tmp_l2_pipe.add_otb_app(atb_concat_app)
# ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **
# START WRITING MSK Image file DATA
# ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **
# Connect the WAM image
was_resampled = os.path.join(working_dir, "was_resampled_" + l_StrResolution + ".tif")
app_resample_was = resample(self._wasimage, self._dtm.ALTList[resol],
was_resampled, threshold=0.25, write_output=False)
tmp_l2_pipe.add_otb_app(app_resample_was)
# Connect the HID image
hid_resampled = os.path.join(working_dir, "hid_resampled_" + l_StrResolution + ".tif")
app_resample_hid = resample(self._dtm_hid, self._dtm.ALTList[resol],
hid_resampled, threshold=0.25, write_output=False)
tmp_l2_pipe.add_otb_app(app_resample_hid)
# Connect the SHDimage
shd_resampled = os.path.join(working_dir, "shd_resampled_" + l_StrResolution + ".tif")
app_resample_shd = resample(self._dtm_shd, self._dtm.ALTList[resol],
shd_resampled, threshold=0.25, write_output=False)
tmp_l2_pipe.add_otb_app(app_resample_shd)
# Create the MOTH image that concatenates the WAT, HID, SHD, STL and TGS masks
MOTHImageList = []
MOTHImageList.append(app_resample_was.getoutput().get("out"))
MOTHImageList.append(app_resample_hid.getoutput().get("out"))
MOTHImageList.append(app_resample_shd.getoutput().get("out"))
# Append STL
MOTHImageList.append(self._stl_list[resol])
# Append TGS
MOTHImageList.append(self._tgs_list[resol])
app_resample_snow = None
if self._cld_snow is not None:
snow_resampled = os.path.join(working_dir, "snow_resampled_" + l_StrResolution + ".tif")
app_resample_snow = resample(self._cld_snow, self._dtm.ALTList[resol],
snow_resampled, working_dir, 0.25, write_output=False)
tmp_l2_pipe.add_otb_app(app_resample_snow)
MOTHImageList.append(app_resample_snow.getoutput().get("out"))
# Concat to get atb
moth_tmp_concat = os.path.join(working_dir, "tmp_moth_" + l_StrResolution + ".tif")
param_moth_concat = {"il": MOTHImageList,
"out": moth_tmp_concat
}
# Concatenate to produce the MOTH file
app_moth_concat = OtbAppHandler("ConcatenateMaskImages", param_moth_concat, write_output=False)
tmp_l2_pipe.add_otb_app(app_moth_concat)
# Binary concatenation of WAT, HID, SHD, STL and TGS masks
msk_filename = p_L2ImageFilenamesProvider.get_msk_filename()[resol]
param_moth_binconcat = {"im": app_moth_concat.getoutput().get("out"),
"out": msk_filename + ":uint8"
}
moth_binconcat_app = OtbAppHandler("BinaryConcatenate", param_moth_binconcat,
write_output=is_croco_on("earthexplorer.l2writer.msk"))
tmp_l2_image_list.append(moth_binconcat_app.getoutput().get("out"))
tmp_l2_filename_list.append(msk_filename)
tmp_l2_pipe.add_otb_app(moth_binconcat_app)
# Concatenation of the MSK mask with the CLD and MOTH masks
# --------------------------------------------------------
# ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **
# START WRITING QLT Image file DATA
# ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** **
QOTHImageList = []
QOTHImageList.append(self._l2edgimagelist[resol])
QOTHImageList.append(self._l2taomasklist[resol])
if self._plugin.WaterVapourDetermination:
QOTHImageList.append(self._l2iwcmasklist[resol])
# Concat to get atb
qoth_tmp_concat = os.path.join(working_dir, "tmp_qoth_" + l_StrResolution + ".tif")
param_qoth_concat = {"il": QOTHImageList,
"out": qoth_tmp_concat + ":uint8"
}
qoth_concat_app = OtbAppHandler("ConcatenateImages", param_qoth_concat, write_output=False)
qoth_tmp_binconcat = os.path.join(working_dir, "tmp_binqoth_" + l_StrResolution + ".tif")
param_qoth_binconcat = {"im": qoth_concat_app.getoutput().get("out"),
"out": qoth_tmp_binconcat + ":uint8"
}
qoth_binconcat_app = OtbAppHandler("BinaryConcatenate", param_qoth_binconcat, write_output=False)
# -------------------------------------------------------
# Concatenation of the QLT mask with the SAT, PIX and OTH masks
# --------------------------------------------------------
# As for the PIX mask, the SAT mask in concatenate in one band where each bit matches one band
sat_tmp_concat = os.path.join(working_dir, "tmp_sat_" + l_StrResolution + ".tif")
param_sat_binconcat = {"im": self._l2satimagelist[resol],
"out": sat_tmp_concat + ":uint8"
}
sat_binconcat_app = OtbAppHandler("BinaryConcatenate", param_sat_binconcat, write_output=False)
# Create the QLT vector image
qlt_tmp_concat = os.path.join(working_dir, "tmp_qlt_" + l_StrResolution + ".tif")
QLTImageList = []
QLTImageList.append(sat_binconcat_app.getoutput().get("out"))
QLTImageList.append(self._l2piximagelist[resol])
QLTImageList.append(qoth_binconcat_app.getoutput().get("out"))
param_qlt_concat = {"il": QLTImageList,
"out": qlt_tmp_concat + ":uint8"
}
qlt_concat_app = OtbAppHandler("ConcatenateImages", param_qlt_concat,
write_output=is_croco_on("earthexplorer.l2writer.qlt"))
tmp_l2_image_list.append(qlt_concat_app.getoutput().get("out"))
tmp_l2_filename_list.append(p_L2ImageFilenamesProvider.get_qlt_filenames()[resol])
tmp_l2_pipe.add_otb_app(qlt_concat_app)
# --------------------------
# Write all the images at L2 Reso
write_images(tmp_l2_image_list, tmp_l2_filename_list)
tmp_l2_pipe.free_otb_app()
clean_pipe(self._sre_list[resol])
if p_EnvCorOption:
clean_pipe(self._fre_list[resol])
# ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** *
# START WRITING CLD Public Image file DATA
# ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** *
# Connect the CLD image
# -------------------------------------
self.write_cld_image(self._l2cldlist[resol], p_CLDDataBandsSelected,
p_L2ImageFilenamesProvider.get_cld_image_filename()[resol])
LOGGER.debug("Writing L2 resolution image done !")
def write_public_images(self, p_PublicDirectory, p_L2BaseFilename,
p_ReflectanceQuantificationValue,
p_AOTQuantificationValue, p_AOTNodataValue,
p_VAPQuantificationValue, p_VAPNodataValue,
p_CLDDataBandsSelected, p_CLDCoreAlgorithmsMapBand,
p_WritePublicProduct, p_EnvCorOption, working_dir):
# IF PUBLIC PART OF L2 PRODUCT IS WRITTEN
if p_WritePublicProduct:
l_BandsDefinitions = self._plugin.BandsDefinitions
l_ListOfL2Res = l_BandsDefinitions.ListOfL2Resolution
l_NumberOfResolutions = len(l_ListOfL2Res)
LOGGER.debug(
"L2ImageFileWriterBase::Initialize Number of resolutions: " +
str(l_NumberOfResolutions) + ".")
# Not use the list of XS band because for Landsat8, the band B9 is not
# selected in the L2 resolution (only in L2Coarse resolution)
l_BaseL2FullFilename = os.path.join(p_PublicDirectory,
p_L2BaseFilename)
l_MASKSDirectory = os.path.join(p_PublicDirectory, "MASKS")
l_BaseL2FullMASKSFilename = os.path.join(l_MASKSDirectory,
p_L2BaseFilename)
file_utils.create_directory(l_MASKSDirectory)
resol_QLK = 0
l_RedBandId, l_BlueBandId, l_GreenBandId = l_BandsDefinitions.get_l2_information_for_quicklook_band_code(
self._quicklookredbandcode, self._quicklookgreenbandcode,
self._quicklookbluebandcode)
# *************************************************************************************************************
# **** LOOP on RESOLUTION *********************************************
# *************************************************************************************************************
for resol in range(0, l_NumberOfResolutions):
l_StrResolution = l_BandsDefinitions.ListOfL2Resolution[resol]
# --------------------------------------------------------
# L2 area AreaType l_AreaToL2Resolution
l_AreaFile = self._sre_list[resol]
l_ListOfBand = l_BandsDefinitions.get_list_of_l2_band_code(
l_StrResolution)
l_NumberOfBands = len(l_ListOfBand)
LOGGER.debug(
"L2ImageFileReader::Gen Public image file for the resolution "
+ l_StrResolution + ".")
l_grpSuffix = ""
if l_NumberOfResolutions == 1:
l_grpSuffix = "XS"
else:
l_grpSuffix = l_ListOfL2Res[resol]
l_StrResolution = l_BandsDefinitions.ListOfL2Resolution[resol]
# Read the Coef apply for SRE and FRE images
LOGGER.info(
"SRE and FRE values multiply by the reflectance quantification value "
+ str(p_ReflectanceQuantificationValue) + ".")
# *************************************************************************************************************
# **** PUBLIC DATA ************************************************
# *************************************************************************************************************
# START WRITING SRE Image file DATA
# Initialize the Scalar filter
sre_pipeline = OtbPipelineManager()
#Extract each channel for each file
tmp_l2_filename_list = []
tmp_l2_image_list = []
tmp_sre_scaled = os.path.join(
working_dir,
"tmp_sre_multi_round_" + l_StrResolution + ".tif")
param_scaled_sre = {
"im": self._sre_list[resol],
"coef": p_ReflectanceQuantificationValue,
"out": tmp_sre_scaled
}
scaled_sre_app = OtbAppHandler("RoundImage",
param_scaled_sre,
write_output=False)
sre_pipeline.add_otb_app(scaled_sre_app)
for i in range(l_NumberOfBands):
if resol == resol_QLK and (l_RedBandId == i
or l_GreenBandId == i
or l_BlueBandId == i):
tmp_sre_roi = os.path.join(
working_dir,
"tmp_sre_roi_" + l_ListOfBand[i] + ".tif")
tmp_sre_roi_app = extract_roi(self._sre_list[resol],
[i],
tmp_sre_roi,
write_output=False)
tmp_l2_image_list.append(
tmp_sre_roi_app.getoutput().get("out"))
tmp_l2_filename_list.append(tmp_sre_roi)
if l_RedBandId == i:
self._qckl_red_image = tmp_sre_roi
elif l_GreenBandId == i:
self._qckl_green_image = tmp_sre_roi
elif l_BlueBandId == i:
self._qckl_blue_image = tmp_sre_roi
sre_pipeline.add_otb_app(tmp_sre_roi_app)
tmp_sre_scaled_roi = os.path.join(
working_dir,
"tmp_sre_scaled_roi_" + l_ListOfBand[i] + ".tif")
tmp_sre_scaled_roi_app = extract_roi(
scaled_sre_app.getoutput().get("out"), [i],
tmp_sre_scaled_roi + ":int16",
write_output=False)
tmp_l2_image_list.append(
tmp_sre_scaled_roi_app.getoutput().get("out"))
sre_pipeline.add_otb_app(tmp_sre_scaled_roi_app)
tmp_l2_filename_list.append(
l_BaseL2FullFilename + "_SRE_" + l_ListOfBand[i] +
".tif" + file_utils.
get_extended_filename_write_image_file_standard())
# START WRITING FRE Image file DATA
tmp_tgs_filename = os.path.join(
working_dir, "tmp_tgs_" + l_StrResolution + ".tif")
tmp_stl_filename = os.path.join(
working_dir, "tmp_stl_" + l_StrResolution + ".tif")
fre_pipeline = OtbPipelineManager()
if p_EnvCorOption:
# Initialize the Scalar filter
tmp_fre_scaled = os.path.join(
working_dir,
"tmp_fre_multi_round_" + l_StrResolution + ".tif")
param_scaled_fre = {
"im": self._fre_list[resol],
"coef": p_ReflectanceQuantificationValue,
"out": tmp_fre_scaled
}
scaled_fre_app = OtbAppHandler("RoundImage",
param_scaled_fre,
write_output=False)
fre_pipeline.add_otb_app(scaled_fre_app)
# Extract each channel for each file
for i in range(l_NumberOfBands):
tmp_fre_roi = os.path.join(
working_dir,
"tmp_fre_roi_" + l_ListOfBand[i] + ".tif")
tmp_fre_roi_app = extract_roi(
scaled_fre_app.getoutput().get("out"), [i],
tmp_fre_roi + ":int16",
write_output=False)
tmp_l2_image_list.append(
tmp_fre_roi_app.getoutput().get("out"))
fre_pipeline.add_otb_app(tmp_fre_roi_app)
tmp_l2_filename_list.append(
l_BaseL2FullFilename + "_FRE_" + l_ListOfBand[i] +
".tif" + file_utils.
get_extended_filename_write_image_file_standard())
#Add tgs and stl also provided by the envcorr
tmp_l2_image_list.append(self._tgs_list[resol])
tmp_l2_filename_list.append(tmp_tgs_filename)
tmp_l2_image_list.append(self._stl_list[resol])
tmp_l2_filename_list.append(tmp_stl_filename)
# START WRITING ATB Image file DATA
# Initialize the Scalar filter
# FA1424: Temporary Fix to address cosmetic aspects of FA1424
# VAPThreshold.SetInput(self.GetVAPImageList()[resol))
# VAPThreshold.SetOutsideValue(255. / p_VAPQuantificationValue)
# VAPThreshold.ThresholdAbove(255. / p_VAPQuantificationValue)
# VAPScalar.SetInput(VAPThreshold.GetOutput())
# VAPScalar.SetCoef(p_VAPQuantificationValue)
atb_pipeline = OtbPipelineManager()
tmp_vap = os.path.join(
working_dir, "tmp_vap_scaled_" + l_StrResolution + ".tif")
param_bandmath_vap = {
"il":
[self._l2vapimagelist[resol], self._l2edgimagelist[resol]],
"exp":
"(im2b1 == 1)?" + str(p_VAPNodataValue) + ":" + "im1b1*" +
str(p_VAPQuantificationValue),
"out":
tmp_vap
}
vap_scal_app = OtbAppHandler("BandMathDouble",
param_bandmath_vap,
write_output=False)
atb_pipeline.add_otb_app(vap_scal_app)
tmp_round_vap = os.path.join(
working_dir, "tmp_vap_round_" + l_StrResolution + ".tif")
param_round_vap = {
"im": vap_scal_app.getoutput().get("out"),
"out": tmp_round_vap
}
vap_round_app = OtbAppHandler("RoundImage",
param_round_vap,
write_output=False)
atb_pipeline.add_otb_app(vap_round_app)
tmp_aot = os.path.join(
working_dir, "tmp_aot_scaled_" + l_StrResolution + ".tif")
param_bandmath_aot = {
"il":
[self._l2aotlist[resol], self._l2edgimagelist[resol]],
"exp":
"(im2b1 == 1)?" + str(p_AOTNodataValue) + ":" + "im1b1*" +
str(p_AOTQuantificationValue),
"out":
tmp_aot
}
aot_scal_app = OtbAppHandler("BandMathDouble",
param_bandmath_aot,
write_output=False)
atb_pipeline.add_otb_app(aot_scal_app)
tmp_round_aot = os.path.join(
working_dir, "tmp_aot_round_" + l_StrResolution + ".tif")
param_round_aot = {
"im": aot_scal_app.getoutput().get("out"),
"out": tmp_round_aot
}
aot_round_app = OtbAppHandler("RoundImage",
param_round_aot,
write_output=False)
atb_pipeline.add_otb_app(aot_round_app)
atb_filename = l_BaseL2FullFilename + "_ATB_" + l_grpSuffix + ".tif"
param_atb_concat = {
"il": [
vap_round_app.getoutput().get("out"),
aot_round_app.getoutput().get("out")
],
"out":
atb_filename + ":uint8" + file_utils.
get_extended_filename_write_image_file_standard()
}
atb_binconcat_app = OtbAppHandler("ConcatenateImages",
param_atb_concat,
write_output=False)
#tmp_l2_image_list.append(atb_binconcat_app.getoutput().get("out"))
#tmp_l2_filename_list.append(atb_filename)
atb_pipeline.add_otb_app(atb_binconcat_app)
# START WRITING IAB MASK
iab_pipeline = OtbPipelineManager()
# Create the image list
tmp_iab = os.path.join(
working_dir, "tmp_iab_concat_" + l_StrResolution + ".tif")
param_iab_concat = {
"il":
[self._l2iwcmasklist[resol], self._l2taomasklist[resol]],
"out": tmp_iab + ":uint8"
}
tmp_iab_concat_app = OtbAppHandler("ConcatenateImages",
param_iab_concat,
write_output=False)
iab_pipeline.add_otb_app(tmp_iab_concat_app)
iab_filename = l_BaseL2FullMASKSFilename + "_IAB_" + l_grpSuffix + ".tif"
param_iab_binconcat = {
"im":
tmp_iab_concat_app.getoutput().get("out"),
"out":
iab_filename + ":uint8" + file_utils.
get_extended_filename_write_image_file_standard()
}
iab_binconcat_app = OtbAppHandler("BinaryConcatenate",
param_iab_binconcat,
write_output=False)
iab_pipeline.add_otb_app(iab_binconcat_app)
write_images([
atb_binconcat_app.getoutput().get("out"),
iab_binconcat_app.getoutput().get("out")
], [atb_filename, iab_filename])
#tmp_l2_image_list.append(iab_binconcat_app.getoutput().get("out"))
#tmp_l2_filename_list.append(iab_filename)
# START WRITING EDG Image file DATA
tmp_l2_image_list.append(self._l2edgimagelist[resol])
tmp_l2_filename_list.append(l_BaseL2FullMASKSFilename +
"_EDG_" + l_grpSuffix + ".tif")
#--------------------------
# Write all the images at L2 Reso
write_images(tmp_l2_image_list, tmp_l2_filename_list)
atb_pipeline.free_otb_app()
iab_pipeline.free_otb_app()
fre_pipeline.free_otb_app()
sre_pipeline.free_otb_app()
# --------------------------
#Caching of cloud images needed for MG2
l_cm2_index = p_CLDCoreAlgorithmsMapBand[CLOUD_MASK_ALL_CLOUDS]
l_shadows_index = p_CLDCoreAlgorithmsMapBand[
CLOUD_MASK_SHADOWS]
l_shadvar_index = p_CLDCoreAlgorithmsMapBand[
CLOUD_MASK_SHADVAR]
# START WRITING MG2 Image file DATA
l_mg2_image_list = []
mg2_pipeline = OtbPipelineManager()
# Connect the WAM image
wat_resampled = os.path.join(
working_dir, "wat_resampled_" + l_StrResolution + ".tif")
app_resample_wat = resample(self._wasimage,
self._dtm.ALTList[resol],
wat_resampled,
method=OtbResampleType.LINEAR,
threshold=0.25,
write_output=False)
l_mg2_image_list.append(
app_resample_wat.getoutput().get("out"))
mg2_pipeline.add_otb_app(app_resample_wat)
# Extract the CM2 cloud mask
l_mg2_image_list.append(self._l2cldlist[resol][l_cm2_index])
#Connect the SNW image if any
if self._cld_snow is not None:
LOGGER.debug(
"Snow mask has been successfully computed, adding it to the MG2"
)
snw_resampled = os.path.join(
working_dir,
"snw_resampled_" + l_StrResolution + ".tif")
app_resample_snw = resample(self._cld_snow,
self._dtm.ALTList[resol],
snw_resampled,
method=OtbResampleType.LINEAR,
threshold=0.25,
write_output=False)
l_mg2_image_list.append(
app_resample_snw.getoutput().get("out"))
mg2_pipeline.add_otb_app(app_resample_snw)
else:
# Add a constant mask
tmp_constant_filename = os.path.join(
working_dir, "Const_shd_masks.tif")
cst_snw_app = constant_image(self._dtm.ALTList[resol],
0,
tmp_constant_filename +
":uint8",
write_output=False)
l_mg2_image_list.append(cst_snw_app.getoutput().get("out"))
mg2_pipeline.add_otb_app(cst_snw_app)
# Connect the shadow or mask
tmp_shador_bandmath = os.path.join(
working_dir,
"tmp_shador_bandmath_" + l_StrResolution + ".tif")
tmp_band_math_app = band_math([
self._l2cldlist[resol][l_shadows_index],
self._l2cldlist[resol][l_shadvar_index]
],
"im1b1 || im2b1",
tmp_shador_bandmath + ":uint8",
write_output=False)
l_mg2_image_list.append(
tmp_band_math_app.getoutput().get("out"))
mg2_pipeline.add_otb_app(tmp_band_math_app)
# Connect the HID image
hid_resampled = os.path.join(
working_dir, "hid_resampled_" + l_StrResolution + ".tif")
app_resample_hid = resample(self._dtm_hid,
self._dtm.ALTList[resol],
hid_resampled,
method=OtbResampleType.LINEAR,
threshold=0.25,
write_output=False)
l_mg2_image_list.append(
app_resample_hid.getoutput().get("out"))
mg2_pipeline.add_otb_app(app_resample_hid)
# Connect the SHDimage
shd_resampled = os.path.join(
working_dir, "shd_resampled_" + l_StrResolution + ".tif")
app_resample_shd = resample(self._dtm_shd,
self._dtm.ALTList[resol],
shd_resampled,
method=OtbResampleType.LINEAR,
threshold=0.25,
write_output=False)
mg2_pipeline.add_otb_app(app_resample_shd)
l_mg2_image_list.append(
app_resample_shd.getoutput().get("out"))
if p_EnvCorOption:
# Append STL
l_mg2_image_list.append(tmp_stl_filename)
# Append TGS
l_mg2_image_list.append(tmp_tgs_filename)
else:
# Append STL
l_mg2_image_list.append(self._stl_list[resol])
# Append TGS
l_mg2_image_list.append(self._tgs_list[resol])
# Concatenate all
tmp_mg2 = os.path.join(
working_dir, "tmp_mg2_concat_" + l_StrResolution + ".tif")
param_mg2_concat = {"il": l_mg2_image_list, "out": tmp_mg2}
tmp_mg2_concat_app = OtbAppHandler("ConcatenateImages",
param_mg2_concat,
write_output=False)
param_mg2_binconcat = {
"im":
tmp_mg2_concat_app.getoutput().get("out"),
"out":
l_BaseL2FullMASKSFilename + "_MG2_" + l_grpSuffix +
".tif" + ":uint8" + file_utils.
get_extended_filename_write_image_file_standard()
}
mg2_binconcat_app = OtbAppHandler("BinaryConcatenate",
param_mg2_binconcat,
write_output=True)
mg2_pipeline.add_otb_app(mg2_binconcat_app)
mg2_pipeline.free_otb_app()
# START WRITING SAT Image file DATA
# TODO Create the writer with test on number of bands
param_sat_binconcat = {
"im":
self._l2satimagelist[resol],
"out":
l_BaseL2FullMASKSFilename + "_SAT_" + l_grpSuffix +
".tif" + ":uint8" + file_utils.
get_extended_filename_write_image_file_standard()
}
sat_binconcat_app = OtbAppHandler("BinaryConcatenate",
param_sat_binconcat,
write_output=True)
# START WRITING PIX Image file DATA
if "PIXImages" in self._l1_image_info.MuscateData:
LOGGER.debug(
"The L1 product have 'Aberrant_Pixels' masks. There are writed in the L2 out product..."
)
otb_file_utils.otb_copy_image_to_file(
self._l2piximagelist[resol],
l_BaseL2FullMASKSFilename + "_PIX_" + l_grpSuffix +
".tif")
else:
LOGGER.debug("No PIX node detected to write")
# START WRITING USI Image file DATA
if "Node_Useful_Image" in self._l1_image_info.MuscateData:
LOGGER.debug(
"The L1 product have 'Useful_Image' files. There are copied in the L2 out product..."
)
# Write the USI in the Xml file, in the Useful_Imagenode !
l_XPathRootUSI_In = "//Mask[Mask_Properties/NATURE='Useful_Image']/Mask_File_List/MASK_FILE[@group_id='{}']"
xnodes_in = xml_tools.get_all_values(
self._l1_image_info.MuscateData["Node_Useful_Image"],
l_XPathRootUSI_In.format(l_grpSuffix))
# Get the path in the xml product filename
lPath = os.path.dirname(self._l1_image_info.HeaderFilename)
for node_in in xnodes_in:
l_FullPathFilename = os.path.join(lPath, node_in.text)
# Expand threshold the file
usi_resampled = l_BaseL2FullMASKSFilename + "_USI_" + l_grpSuffix + ".tif"\
+ file_utils.get_extended_filename_write_mask_file_muscate()
resample(l_FullPathFilename,
self._dtm.ALTList[resol],
usi_resampled,
threshold=0.25,
method=OtbResampleType.LINEAR,
write_output=True)
else:
LOGGER.debug(
"No 'Useful_Image' mask detected in the L1 product."
)
# Fin si manage USI
# START WRITING DFP Image file DATA (=DFP in MUSCATE) #TODO
if self._l2dfpimagelist is not None:
param_dfp_binconcat = {
"im":
self._l2dfpimagelist[resol],
"out":
l_BaseL2FullMASKSFilename + "_DFP_" + l_grpSuffix +
".tif"
}
dfp_binconcat_app = OtbAppHandler("BinaryConcatenate",
param_dfp_binconcat,
write_output=True)
else:
LOGGER.debug("DFP Masks not available.")
# START WRITING CLM (CLD) Image file DATA
# Connect the CLD image
# Connect the CLD image
# -------------------------------------
l_cld_uses_filenames = False
for f in self._l2cldlist[resol]:
if not otb_is_swig_pointer(f) and os.path.exists(f):
l_cld_uses_filenames = True
self.write_cld_image(self._l2cldlist[resol],
p_CLDDataBandsSelected,
l_BaseL2FullMASKSFilename + "_CLM_" +
l_grpSuffix + ".tif",
use_filenames=l_cld_uses_filenames)
LOGGER.debug("Writing L2 resolution image done !")
class MajaCirrusCorrection(MajaModule):
"""
classdocs
"""
NAME = "CirrusCorrection"
def __init__(self):
"""
Constructor
"""
super(MajaCirrusCorrection, self).__init__()
self.in_keys_to_check = [
"Params.Caching", "AppHandler", "Plugin", "L1Reader", "L2COMM",
"DEM"
]
self.out_keys_to_check = []
self.out_keys_provided = []
self._l2_pipeline = OtbPipelineManager()
def run(self, dict_of_input, dict_of_output):
LOGGER.info("Cirrus Correction start")
cirrus_working = dict_of_input.get(
"AppHandler").get_directory_manager().get_temporary_directory(
"CirrusCorrProc_", do_always_remove=True)
refres_name = dict_of_input.get(
"Plugin").CirrusCorrectionReferenceResolution
refres_idx = 9999
bands_definition = dict_of_input.get("Plugin").BandsDefinitions
for i in range(0, len(bands_definition.ListOfL2Resolution)):
if bands_definition.ListOfL2Resolution[i] == refres_name:
refres_idx = i
# TODO test refres_idx
result = {}
ref_result = self._gamma_compute_at_res(dict_of_input, dict_of_output,
refres_idx, cirrus_working)
result[bands_definition.ListOfL2Resolution[refres_idx]] = ref_result
if ref_result[2]:
LOGGER.info("Cirrus flag enabled on reference resolution")
l_gammamean = ref_result[3] * ref_result[4]
l_nbgamma = ref_result[3]
for i in range(0, len(bands_definition.ListOfL2Resolution)):
if bands_definition.ListOfL2Resolution[i] != refres_name:
res_result = self._gamma_compute_at_res(
dict_of_input, dict_of_output, i, cirrus_working)
result[bands_definition.ListOfL2Resolution[i]] = res_result
l_gammamean = l_gammamean + res_result[3] * res_result[4]
l_nbgamma = l_nbgamma + res_result[3]
l_gammamean = l_gammamean / l_nbgamma
l_gamma_max = float(
dict_of_input.get("L2COMM").get_value(
"CirrusCorrectionMaxGammaValue"))
l_gamma_min = float(
dict_of_input.get("L2COMM").get_value(
"CirrusCorrectionMinGammaValue"))
# Boundary set for gamma
if l_gammamean > l_gamma_max:
l_gammamean = l_gamma_max
if l_gammamean < l_gamma_min:
l_gammamean = l_gamma_min
LOGGER.info("Gamma : " + str(l_gammamean))
# Apply correction
for i in range(0, len(bands_definition.ListOfL2Resolution)):
l_res = bands_definition.ListOfL2Resolution[i]
corrected_toa = os.path.join(cirrus_working,
"toac_" + l_res + ".tif")
l_gammaswir = l_gammamean * float(
dict_of_input.get("L2COMM").get_value(
"CirrusCorrectionSWIRGammaFactor"))
l_l2bandcodes = bands_definition.get_list_of_l2_band_code(
l_res)
swir_band_list = xml_tools.as_string_list(
dict_of_input.get("L2COMM").get_value(
"CirrusCorrectionSWIRBandCodes"))
# Param of cirrus apply app
param_cirrus_apply = {
"l2toa":
dict_of_output["AtmoAbsIPTOA_" + l_res],
"l2edg":
dict_of_input.get("L1Reader").get_value("L2EDGOutputList")
[i],
"l2dtm":
dict_of_input.get("DEM").ALTList[i],
"l2cirrus":
result[l_res][0],
"cirrusmask":
result[l_res][1],
"resolution":
bands_definition.get_l1_resolution(l_res),
"maxdistance":
float(
dict_of_input.get("L2COMM").get_value(
"CirrusCorrectionMaxDistance")),
"thresholdgain":
float(
dict_of_input.get("L2COMM").get_value(
"CirrusMaskThresholdGain")),
"thresholdoffset":
float(
dict_of_input.get("L2COMM").get_value(
"CirrusMaskThresholdOffset")),
"thresholdfactor":
float(
dict_of_input.get("L2COMM").get_value(
"CirrusCorrectionThresholdFactor")),
"gammamean":
l_gammamean,
"swirgammafactor":
l_gammaswir,
"maxreflectance":
float(
dict_of_input.get("L2COMM").get_value(
"CirrusMaxReflectance")),
"bandcodelist":
l_l2bandcodes,
"swirbandcodelist":
swir_band_list,
"correctedtoa":
corrected_toa
}
app = OtbAppHandler(
"CirrusCorrectionApply",
param_cirrus_apply,
write_output=(False or is_croco_on("cirruscorrection")))
self._l2_pipeline.add_otb_app(app)
dict_of_output["L2TOA_" +
l_res] = app.getoutput().get("correctedtoa")
else:
LOGGER.info(
"Cirrus flag disabled on reference resolution, no cirrus correction"
)
# return cirrus corrected flag
dict_of_output["CirrusCorrected"] = ref_result[2]
return ref_result[2]
def _gamma_compute_at_res(self, dict_of_input, dict_of_output, p_res,
p_working):
bands_definition = dict_of_input.get("Plugin").BandsDefinitions
l_res = bands_definition.ListOfL2Resolution[p_res]
LOGGER.info("Cirrus gamma compute at res " + l_res + " start")
# ---------- Resample L1 Cirrus to resolution ---------------
l1cirrus_resampled = os.path.join(p_working,
"toacirrus_" + l_res + ".tif")
param_l1cirrus_resample = {
"dtm": dict_of_input.get("DEM").ALTList[p_res],
"im": dict_of_input.get("L1Reader").get_value("L1TOACirrusImage"),
"interp": "bco",
"out": l1cirrus_resampled
}
app_l1cirrus_resamp = OtbAppHandler("Resampling",
param_l1cirrus_resample,
write_output=True)
# ---------- Resample Cirrus cloud to resolution ---------------
cld_cirrus_resampled = dict_of_output[CLOUD_MASK_CIRRUS + "_" + l_res]
cirrusmask_resampled = os.path.join(p_working,
"cirrusmask_" + l_res + ".tif")
# ---------- Resample All cloud to resolution ---------------
cld_all_resampled = dict_of_output[CLOUD_MASK_ALL + "_" + l_res]
allmask_resampled = os.path.join(p_working,
"allmask_" + l_res + ".tif")
# ---------- Resample Refl cloud to resolution ---------------
cld_refl_resampled = dict_of_output[CLOUD_MASK_REFL + "_" + l_res]
allreflresampled = os.path.join(p_working,
"reflmask_" + l_res + ".tif")
# ----- Caching of L2TOA
l2toa_cach = os.path.join(p_working, "l2toa_" + l_res + ".tif")
#Write to caching
write_images([
cld_cirrus_resampled, cld_all_resampled, cld_refl_resampled,
dict_of_output["L2TOA_" + l_res]
], [
cirrusmask_resampled, allmask_resampled, allreflresampled,
l2toa_cach
])
# Update dict
clean_pipe(dict_of_output[CLOUD_MASK_CIRRUS + "_" + l_res])
clean_pipe(dict_of_output[CLOUD_MASK_ALL + "_" + l_res])
clean_pipe(dict_of_output[CLOUD_MASK_REFL + "_" + l_res])
clean_pipe(dict_of_output["L2TOA_" + l_res])
dict_of_output[CLOUD_MASK_CIRRUS + "_" + l_res] = cirrusmask_resampled
dict_of_output[CLOUD_MASK_ALL + "_" + l_res] = allmask_resampled
dict_of_output[CLOUD_MASK_REFL + "_" + l_res] = allreflresampled
dict_of_output["L2TOA_" + l_res] = l2toa_cach
# --------------------- Gamma compute for the resolution
# construct band list
gamm_band_list = xml_tools.as_string_list(
dict_of_input.get("L2COMM").get_value(
"CirrusCorrectionGammaBandCodes"))
l_l2bandcodes = bands_definition.get_list_of_l2_band_code(l_res)
l_resbandlist = []
for i in range(0, len(l_l2bandcodes)):
if l_l2bandcodes[i] in gamm_band_list:
l_resbandlist.append(str(i))
cirrus_mask = os.path.join(p_working,
"cirrus_corr_mask_" + l_res + ".tif")
param_gamma_compute = {
"l2toa":
l2toa_cach,
"l2edg":
dict_of_input.get("L1Reader").get_value("L2EDGOutputList")[p_res],
"l2dtm":
dict_of_input.get("DEM").ALTList[p_res],
"l2cirrus":
app_l1cirrus_resamp.getoutput().get("out"),
"bandlist":
l_resbandlist,
"cloudcirrus":
cirrusmask_resampled,
"cloudall":
allmask_resampled,
"cloudrefl":
allreflresampled,
"cirrusminpixinlist":
int(
dict_of_input.get("L2COMM").get_value(
"CirrusCorrectionMinPixInList")),
"mask":
cirrus_mask + ":uint8"
}
gamma_compute_app = OtbAppHandler("GammaCompute",
param_gamma_compute,
write_output=True)
cirr_flag = bool(gamma_compute_app.getoutput()["cirrusflag"])
gamma = float(gamma_compute_app.getoutput()["gamma"])
del gamma_compute_app
del app_l1cirrus_resamp
return [
l1cirrus_resampled, cirrus_mask, cirr_flag,
len(l_resbandlist), gamma
]
class MajaAthmosphericAbsorption(MajaModule):
"""
classdocs
"""
NAME = "AthmosphericAbsorption"
def __init__(self):
"""
Constructor
"""
super(MajaAthmosphericAbsorption, self).__init__()
self._coarse_pipeline = OtbPipelineManager()
self._l2_pipeline = OtbPipelineManager()
self.in_keys_to_check = ["Params.WriteL2ProductToL2Resolution", "Params.RealL2NoData",
"Params.OzoneAmount",
"AppHandler", "Plugin", "L1Reader", "L1Info" ,
"L2COMM", "DEM"]
self.out_keys_to_check = []
self.out_keys_provided = ["AtmoAbsIPTOAC"]
def run(self, dict_of_input, dict_of_output):
LOGGER.info("AthmosphericAbsorption start")
atm_working = dict_of_input.get("AppHandler").get_directory_manager().get_temporary_directory("AtmoProc_",
do_always_remove=True)
l_writeL2 = dict_of_input.get("Params").get("WriteL2ProductToL2Resolution")
bands_definition = dict_of_input.get("Plugin").BandsDefinitions
# Get some datas
pressure_image_sub = os.path.join(atm_working, "atmo_pressure_sub.tif")
param_pressure = {"dtm": dict_of_input.get("DEM").ALC,
"pressure": pressure_image_sub}
pressure_app_sub = OtbAppHandler("PressureFilter", param_pressure,
write_output=True)
self._coarse_pipeline.add_otb_app(pressure_app_sub)
viewing_zenith = dict_of_input.get("L1Info").ListOfViewingZenithAnglesPerBandAtL2CoarseResolution
water_amount_image_sub = os.path.join(atm_working, "atmo_wateramount_sub.tif")
if dict_of_input.get("Plugin").WaterVapourDetermination and not as_bool(
dict_of_input.get("L2COMM").get_value("GIP_L2COMM_UseDefaultConstantWaterAmount")):
watervaporreferencebandid = bands_definition.get_band_id_in_l2_coarse(
dict_of_input.get("L2COMM").get_value("WaterVaporReferenceBandCode"))
watervaporbandid = bands_definition.get_band_id_in_l2_coarse(
dict_of_input.get("L2COMM").get_value("WaterVaporBandCode"))
viewing_zenith_vapor_band = float(viewing_zenith[watervaporbandid])
param_watervapour = {"toa": dict_of_input.get("L1Reader").get_value("IPTOASubOutput"),
"watervaporreferencebandid": watervaporreferencebandid,
"watervaporbandid": watervaporbandid,
"vapnodatavalue": float(dict_of_input.get("L2COMM").get_value("VAPNodataValue")),
"watervapormaxthreshold": float(
dict_of_input.get("L2COMM").get_value("WaterVaporMaxThreshold")),
"solarzenith": float(dict_of_input.get("L1Info").SolarAngle["sun_zenith_angle"]),
"viewingzenith": viewing_zenith_vapor_band,
"wateramount": water_amount_image_sub,
"gipwatv": dict_of_input.get("L2WATV").new_gipp_filename}
water_vapour_app_sub = OtbAppHandler("WaterAmountGeneratorFilter", param_watervapour,
write_output=False)
self._coarse_pipeline.add_otb_app(water_vapour_app_sub)
dict_of_output["VAP_Sub"] = water_vapour_app_sub.getoutput()["wateramount"]
toac_image_sub = os.path.join(atm_working, "atmo_toac_sub.tif")
param_toa_correction = {"toa": dict_of_input.get("L1Reader").get_value("IPTOASubOutput"),
"pressure": pressure_app_sub.getoutput()["pressure"],
"smac": dict_of_input.get("L2SMAC").new_gipp_filename,
"wateramountdefault": float(
dict_of_input.get("L2COMM").get_value("WaterAmountDefaultValue")),
"nodata": dict_of_input.get("Params").get("RealL2NoData"),
"ozoneamount": dict_of_input.get("Params").get("OzoneAmount"),
"thetas": float(dict_of_input.get("L1Info").SolarAngle["sun_zenith_angle"]),
"thetav": viewing_zenith,
"bandlist": bands_definition.get_list_of_band_id_in_l2_coarse(),
"toac": toac_image_sub}
if (dict_of_input.get("Plugin").WaterVapourDetermination and
dict_of_input.get("L2COMM").get_value("GIP_L2COMM_UseDefaultConstantWaterAmount")):
param_toa_correction["wateramount"] = dict_of_output["VAP_Sub"]
toa_correction_app_sub = OtbAppHandler("TOACorrection", param_toa_correction, write_output=l_writeL2)
if not l_writeL2:
self._coarse_pipeline.add_otb_app(toa_correction_app_sub)
dict_of_output["AtmoAbsIPTOAC"] = toa_correction_app_sub.getoutput()["toac"]
if dict_of_input.get("Params").get("WriteL2ProductToL2Resolution"):
l_nbRes = len(bands_definition.ListOfL2Resolution)
for r in range(0, l_nbRes):
l_res = bands_definition.ListOfL2Resolution[r]
pressure_image = os.path.join(atm_working, "atmo_pressure_" + l_res + ".tif")
param_pressure["dtm"] = dict_of_input.get("DEM").ALTList[r]
param_pressure["pressure"] = pressure_image
pressure_app = OtbAppHandler("PressureFilter", param_pressure,write_output=False)
self._l2_pipeline.add_otb_app(pressure_app)
water_image = os.path.join(atm_working, "atmo_wateramount_" + l_res + ".tif")
if (dict_of_input.get("Plugin").WaterVapourDetermination and
dict_of_input.get("L2COMM").get_value("GIP_L2COMM_UseDefaultConstantWaterAmount")):
param_resamp = {"dtm": dict_of_input.get("DEM").ALTList[r],
"im": dict_of_output["VAP_Sub"],
"interp": "linear",
"padradius": 4.0,
"out": water_image}
water_app = OtbAppHandler("Resampling", param_resamp, write_output=False)
self._l2_pipeline.add_otb_app(water_app)
param_toa_correction["wateramount"] = water_app.getoutput().get("out")
toac_image = os.path.join(atm_working, "atmo_toa_" + l_res + ".tif")
param_toa_correction["toa"] = dict_of_input.get("L1Reader").get_value("L2TOAImageList")[r]
param_toa_correction["pressure"] = pressure_app.getoutput().get("pressure")
band_list = bands_definition.get_list_of_l2_band_id(l_res)
LOGGER.debug("Athmo band_list")
LOGGER.debug(band_list)
viewing_zenith_l2 = []
band_list_l2 = []
for i in band_list:
viewing_zenith_l2.append(viewing_zenith[i])
band_list_l2.append(str(i))
param_toa_correction["bandlist"] = band_list_l2
param_toa_correction["thetav"] = viewing_zenith_l2
param_toa_correction["toac"] = toac_image
toa_app = OtbAppHandler("TOACorrection", param_toa_correction,write_output=False)
self._l2_pipeline.add_otb_app(toa_app)
dict_of_output["AtmoAbsIPTOA_" + l_res] = toa_app.getoutput().get("toac")
dict_of_output["L2TOA_" + l_res] = toa_app.getoutput().get("toac")
class MajaCloudMaskComputation(MajaModule):
"""
classdocs
"""
NAME = "CloudMask"
def __init__(self):
"""
Constructor
"""
super(MajaCloudMaskComputation, self).__init__()
self.in_keys_to_check = [
"Params.Caching", "Params.BlueBandIndex_DateD",
"Params.RedBandIndex_DateD", "Params.NIRBandIndex_DateD",
"Params.CorrelBandIndex_DateD", "Params.ShadowBandIndex_DateD",
"Params.ReflectanceWaterBandIndex_DateD", "Params.InitMode",
"Params.SWIRBandAvailable", "Params.StoListOfDates", "AppHandler",
"Plugin", "L1Reader", "L2Reader", "L2COMM", "DEM"
]
self.out_keys_to_check = ["SunglintFlag", "RayleighIPTOCR", "CLA_Sub"]
self.out_keys_provided = [
"cld_l2cla", "CLDList", constants.CLOUD_MASK_ALL,
constants.CLOUD_MASK_ALL_CLOUDS, constants.CLOUD_MASK_SHADOWS,
constants.CLOUD_MASK_SHADVAR, constants.CLOUD_MASK_REFL,
constants.CLOUD_MASK_REFL_VAR, constants.CLOUD_MASK_EXTENSION,
constants.CLOUD_MASK_EXTENSION, constants.CLOUD_MASK_ALT,
constants.CLOUD_MASK_CIRRUS
]
self._apps = OtbPipelineManager()
def cleanup(self):
self._apps.free_otb_app()
@staticmethod
def _dilate_and_mask(input, temp, output, mask, radius, caching):
param_dilate = {
"in": input,
"out": temp + ":uint8",
"structype": "ball",
"xradius": radius,
"yradius": radius,
"filter": "dilate"
}
dilate_app = OtbAppHandler("BinaryMorphologicalOperation",
param_dilate,
write_output=caching)
param_mask = {
"il": [dilate_app.getoutput()["out"], mask],
"out": output + ":uint8",
"exp": "(im2b1 !=0)?0:im1b1"
}
mask_app = OtbAppHandler("BandMath", param_mask, write_output=caching)
return mask_app
def run(self, dict_of_input, dict_of_output):
LOGGER.info("Cloud mask computation start")
init_Mode = dict_of_input.get("Params").get("InitMode")
cloud_working = ""
caching = dict_of_input.get("Params").get("Caching")
if caching:
cloud_working = dict_of_input.get(
"AppHandler").get_directory_manager().get_temporary_directory(
"CloudMaskingProc_", do_always_remove=True)
cloud_reflectance_filename = os.path.join(cloud_working,
"cloud_refl.tif")
cloud_reflext_filename = os.path.join(cloud_working,
"cloud_reflext.tif")
cloud_reflvar_filename = os.path.join(cloud_working,
"cloud_reflvar.tif")
const_mask_filename = os.path.join(cloud_working,
"cloud_constzero.tif")
water_treshold = float(
dict_of_input.get("L2COMM").get_value("WaterReflectanceThreshold"))
if dict_of_output.get("SunglintFlag"):
water_treshold = float(
dict_of_input.get("L2COMM").get_value(
"WaterSunglintReflectanceThreshold"))
bluebandtocr_idx = dict_of_input.get("Params").get(
"BlueBandIndex_DateD")
redbandtocr_idx = dict_of_input.get("Params").get("RedBandIndex_DateD")
nirbandtocr_idx = dict_of_input.get("Params").get("NIRBandIndex_DateD")
correlbandtocr_idx = dict_of_input.get("Params").get(
"CorrelBandIndex_DateD")
shadowbandtocr_idx = dict_of_input.get("Params").get(
"ShadowBandIndex_DateD")
reflwaterband_idx = dict_of_input.get("Params").get(
"ReflectanceWaterBandIndex_DateD")
bluebandrcr_idx = 999
redbandrcr_idx = 999
nirbandrcr_idx = 999
correlbandrcr_idx = 999
shadowbandrcr_idx = 999
# ---------------------------------------------------------------
# ------------------- Constant zero mask --------------------------
constant_app = constant_image(dict_of_input.get("DEM").ALC,
0,
const_mask_filename + ":uint8",
write_output=caching)
constant_mask = constant_app.getoutput()["out"]
# ---------------------------------------------------------------
# ------------------- Relfectance app --------------------------
compute_reflvar = False
param_reflectance = {
"tocr":
dict_of_output.get("RayleighIPTOCR"),
"edg":
dict_of_input.get("L1Reader").get_value("IPEDGSubOutput"),
"sat":
dict_of_input.get("L1Reader").get_value("IPSATSubOutput"),
"waterthreshold":
water_treshold,
"bluebandtocr":
bluebandtocr_idx,
"redbandtocr":
redbandtocr_idx,
"nirbandtocr":
nirbandtocr_idx,
"correlbandtocr":
correlbandtocr_idx,
"bluereflectancethresholdvalue":
dict_of_input.get("L2COMM").get_value_f(
"CloudBlueReflectanceThreshold"),
"redreflectancethresholdvalue":
dict_of_input.get("L2COMM").get_value_f(
"CloudRedReflectanceThreshold"),
"nirredratiomaxthresholdvalue":
dict_of_input.get("L2COMM").get_value_f("NirRedRatioMaxThreshold"),
"nirredratiominthresholdvalue":
dict_of_input.get("L2COMM").get_value_f("NirRedRatioMinThreshold"),
"nodata":
dict_of_input.get("Params").get("RealL2NoData"),
"foregroundvalue":
1,
"reflectancewaterband":
reflwaterband_idx,
"refl":
cloud_reflectance_filename + ":uint8"
}
if dict_of_input.get("Params").get("InitMode"):
param_reflectance["initmode"] = dict_of_input.get("Params").get(
"InitMode")
if dict_of_input.get("Params").get("SnowBandAvailable"):
param_reflectance["snow"] = dict_of_output.get("SnowMask")
if dict_of_input.get("Params").get("SWIRBandAvailable"):
bands_definition = dict_of_input.get("Plugin").BandsDefinitions
# Get some datas
swirbandidx = bands_definition.get_band_id_in_l2_coarse(
dict_of_input.get("L2COMM").get_value("SWIR1BandCode"))
param_reflectance["swir.bandtocr"] = swirbandidx
param_reflectance["swir.nirswirratiomin"] = dict_of_input.get(
"L2COMM").get_value_f("NirSwirRatioMinThreshold")
if not init_Mode:
compute_reflvar = True
bluebandrcr_idx = dict_of_input.get("Params").get(
"BlueBandIndex_DateDm1")
redbandrcr_idx = dict_of_input.get("Params").get(
"RedBandIndex_DateDm1")
nirbandrcr_idx = dict_of_input.get("Params").get(
"NIRBandIndex_DateDm1")
correlbandrcr_idx = dict_of_input.get("Params").get(
"CorrelBandIndex_DateDm1")
shadowbandrcr_idx = dict_of_input.get("Params").get(
"ShadowBandIndex_DateDm1")
param_reflectance_nominal = {
"l2was":
dict_of_input.get("L2Reader").get_value("WASImage"),
"l2rcr":
dict_of_input.get("L2Reader").get_value("RCRImage"),
"l2sto":
dict_of_input.get("L2Reader").get_value("STOImage"),
"l2pxd":
dict_of_input.get("L2Reader").get_value("PXDImage"),
"l2ndt":
dict_of_input.get("L2Reader").get_value("NDTImage"),
"nominal.seradius":
dict_of_input.get("L2COMM").get_value_i("SEInputMasksRadius"),
"nominal.bluebandrcr":
bluebandrcr_idx,
"nominal.redbandrcr":
redbandrcr_idx,
"nominal.nirbandrcr":
nirbandrcr_idx,
"nominal.correlbandrcr":
correlbandrcr_idx,
"nominal.jday":
dict_of_input.get("Params").get("JDay"),
"nominal.cloudforgettingduration":
dict_of_input.get("L2COMM").get_value_i(
"CloudForgettingDuration"),
"nominal.minvarthreshold":
dict_of_input.get("L2COMM").get_value_f("MinThresholdVarBlue"),
"nominal.maxvarthreshold":
dict_of_input.get("L2COMM").get_value_f("MaxThresholdVarBlue"),
"nominal.knirblue":
dict_of_input.get("L2COMM").get_value_f("KNIRBlue"),
"nominal.kredblue":
dict_of_input.get("L2COMM").get_value_f("KRedBlue"),
"nominal.deltathreshold":
dict_of_input.get("L2COMM").get_value_f("DeltaThreshold"),
"nominal.minpixelcorrelation":
dict_of_input.get("L2COMM").get_value_f("MinPixelCorrel"),
"nominal.correlthreshold":
float(
dict_of_input.get("L2COMM").get_value_f("CorrelThreshold"))
/ 100.0,
"nominal.ncorrel":
dict_of_input.get("L2COMM").get_value_f("NCorrel"),
"nominal.stolistofdates":
dict_of_input.get("Params").get("StoListOfDates"),
"reflvar":
cloud_reflvar_filename + ":uint8",
"reflext":
cloud_reflext_filename + ":uint8"
}
param_reflectance.update(param_reflectance_nominal)
# Launch the app
reflectance_app = OtbAppHandler("CloudReflectance",
param_reflectance,
write_output=caching)
self._apps.add_otb_app(reflectance_app)
cloud_reflectance_image = reflectance_app.getoutput()["refl"]
cloud_reflext_image = reflectance_app.getoutput()["reflext"]
if not init_Mode:
cloud_reflectvar_image = reflectance_app.getoutput()["reflvar"]
else:
cloud_reflectvar_image = constant_mask
# ------------------- Relfectance app ---------------------------
# ---------------------------------------------------------------
# ---------------------------------------------------------------
# ------------------- Snow app ---------------------------------
tmp_reflectance_image = os.path.join(cloud_working,
"cloud_refl_snow.tif")
if dict_of_input.get("Params").get("SnowBandAvailable"):
tmp_snow_mask = os.path.join(cloud_working, "cloud_snow_mask.tif")
param_snow = {
"snowin":
dict_of_output.get("SnowMask"),
"edg":
dict_of_input.get("L1Reader").get_value("IPEDGSubOutput"),
"cldrefl":
cloud_reflectance_image,
"seradius":
dict_of_input.get("L2COMM").get_value_i("SECloudRadius"),
"refl":
tmp_reflectance_image + ":uint8",
"cldreflvar":
cloud_reflectvar_image,
"snow":
tmp_snow_mask + ":uint8"
}
snow_app = OtbAppHandler("CloudSnowCorrection",
param_snow,
write_output=caching)
self._apps.add_otb_app(snow_app)
cloud_reflectance_image = snow_app.getoutput()["refl"]
dict_of_output["cld_snow"] = snow_app.getoutput()["snow"]
# ------------------- Snow app ---------------------------------
# --------------------------------------------------------------
# ---------------------------------------------------------------
# ------------------- Extension app ---------------------------------
compute_extension = False
cloud_extension_filename = os.path.join(cloud_working,
"cloud_extension.tif")
if not init_Mode and compute_reflvar:
compute_extension = True
param_extension = {
"tocr":
dict_of_output.get("RayleighIPTOCR"),
"edg":
dict_of_input.get("L1Reader").get_value("IPEDGSubOutput"),
"cldext":
cloud_reflext_image,
"cldrefl":
cloud_reflectance_image,
"cldreflvar":
cloud_reflectvar_image,
"l2rcr":
dict_of_input.get("L2Reader").get_value("RCRImage"),
"l2sto":
dict_of_input.get("L2Reader").get_value("STOImage"),
"l2ndt":
dict_of_input.get("L2Reader").get_value("NDTImage"),
"nodata":
dict_of_input.get("Params").get("RealL2NoData"),
"correlbandtocr":
correlbandtocr_idx,
"correlbandrcr":
correlbandrcr_idx,
"jday":
dict_of_input.get("Params").get("JDay"),
"minpixelcorrelation":
dict_of_input.get("L2COMM").get_value_f("MinPixelCorrel"),
"correlthreshold":
float(
dict_of_input.get("L2COMM").get_value_f("CorrelThreshold"))
/ 100.0,
"ncorrel":
dict_of_input.get("L2COMM").get_value_f("NCorrel"),
"discardedratiomin":
float(
dict_of_input.get("L2COMM").get_value_f(
"DiscardedPixelsRatioMin")) / 100.0,
"stolistofdates":
dict_of_input.get("Params").get("StoListOfDates"),
"ext":
cloud_extension_filename + ":uint8"
}
extension_app = OtbAppHandler("CloudExtension",
param_extension,
write_output=caching)
self._apps.add_otb_app(extension_app)
cloud_extension_image = extension_app.getoutput()["ext"]
else:
cloud_extension_image = constant_mask
# ------------------- Extension app ---------------------------------
# ---------------------------------------------------------------
# ---------------------------------------------------------------
# ------------------- Cld all concatenate------------------------
cloud_allnoext_filename = os.path.join(cloud_working,
"cloud_all_noextension.tif")
cloud_all_filename = cloud_allnoext_filename
param_allnoext = {}
if dict_of_input.get("Params").get("CirrusBandAvailable"):
param_allnoext = {
"il": [
cloud_reflectance_image, cloud_reflectvar_image,
dict_of_output.get("CLA_Sub"),
dict_of_output.get("CirrusMask")
],
"exp":
"(im1b1 !=0) || (im2b1!=0) || (im3b1 !=0) || (im4b1 !=0)",
"out":
cloud_allnoext_filename + ":uint8"
}
else:
param_allnoext = {
"il": [
cloud_reflectance_image, cloud_reflectvar_image,
dict_of_output.get("CLA_Sub")
],
"exp":
"(im1b1 !=0) || (im2b1!=0) || (im3b1 !=0)",
"out":
cloud_allnoext_filename + ":uint8"
}
allnoext_app = OtbAppHandler("BandMath",
param_allnoext,
write_output=caching)
self._apps.add_otb_app(allnoext_app)
cloud_allnoext_image = allnoext_app.getoutput()["out"]
cloud_all_image = allnoext_app.getoutput()["out"]
if not init_Mode:
cloud_all_filename = os.path.join(cloud_working, "cloud_all.tif")
param_all = {
"il": [cloud_allnoext_image, cloud_extension_image],
"exp": "(im1b1 !=0) || (im2b1!=0)",
"out": cloud_all_filename + ":uint8"
}
all_app = OtbAppHandler("BandMath",
param_all,
write_output=caching)
self._apps.add_otb_app(all_app)
cloud_all_image = all_app.getoutput()["out"]
# ------------------- Cld all concatenate------------------------
# ---------------------------------------------------------------
# ---------------------------------------------------------------
# ------------------- Cld shadow ------------------------
#caching of ShadowVIE that is know for causing troubles
shadowvie_filename = os.path.join(cloud_working, "shadow_vie.tif")
otb_copy_image_to_file(
dict_of_input.get("L1Reader").get_value("ShadowVIEImage"),
shadowvie_filename)
cloud_shadow_filename = os.path.join(cloud_working, "cloud_shadow.tif")
cloud_cla_filename = os.path.join(cloud_working, "cloud_cla.tif")
grid_ref_alt = dict_of_input.get(
"Plugin").ConfigUserCamera.get_Algorithms(
).get_GRID_Reference_Altitudes()
param_shadow = {
"tocr":
dict_of_output.get("RayleighIPTOCR"),
"edg":
dict_of_input.get("L1Reader").get_value("IPEDGSubOutput"),
"cldall":
cloud_allnoext_image,
"cla":
dict_of_output.get("CLA_Sub"),
"vie":
shadowvie_filename,
"dtm":
dict_of_input.get("DEM").ALC,
"sol1.in":
dict_of_input.get("L1Reader").get_value("SOL1Image"),
"sol1.h":
grid_ref_alt.get_SOLH1(),
"solhref":
grid_ref_alt.get_SOLHRef(),
"defaultalt":
dict_of_input.get("L2COMM").get_value_i("DefaultAltitude"),
"deltahmax":
dict_of_input.get("L2COMM").get_value_i("DeltaHMax"),
"deltahmin":
dict_of_input.get("L2COMM").get_value_i("DeltaHMin"),
"deltahstep":
dict_of_input.get("L2COMM").get_value_i("DeltaHStep"),
"shadbandtocr":
shadowbandtocr_idx,
"shadbandrcr":
shadowbandrcr_idx,
"ksigma":
0,
"viehref":
grid_ref_alt.get_VIEHRef(),
"nodata":
dict_of_input.get("Params").get("RealL2NoData"),
"shadow":
cloud_shadow_filename + ":uint8"
}
if dict_of_input.get("Params").get("InitMode"):
param_shadow["initmode"] = dict_of_input.get("Params").get(
"InitMode")
# With alt shadows
if dict_of_input.get("Params").get("CloudMaskingKnownCloudsAltitude"):
param_shadow["algo"] = "withalt"
param_shadow["ksigma"] = int(
dict_of_input.get("L2COMM").get_value("KsigmaAltitude"))
param_shadow["l2cla"] = cloud_cla_filename + ":int16"
param_shadow["algo.withalt.absnbpixthresh"] = dict_of_input.get(
"L2COMM").get_value_f("AbsNbpixThreshold")
param_shadow["algo.withalt.refinement"] = dict_of_input.get(
"L2COMM").get_value("RefinementOption")
param_shadow["algo.withalt.threshdiffim"] = dict_of_input.get(
"L2COMM").get_value_f("ThresholdDiffImage")
if dict_of_input.get(
"Plugin").GRIDReferenceAltitudesSOL2GridAvailable:
param_shadow["algo.withalt.sol2.in"] = dict_of_input.get(
"L1Reader").get_value("SOL2Image")
param_shadow["algo.withalt.sol2.h"] = grid_ref_alt.get_SOLH2()
else:
param_shadow["algo"] = "withoutalt"
param_shadow["algo.withoutalt.cloudcovermult"] = dict_of_input.get(
"L2COMM").get_value_f("CloudCoverMultFactor")
param_shadow["algo.withoutalt.shadratiomax"] = dict_of_input.get(
"L2COMM").get_value_f("ShadowCloudRatioMax")
param_shadow["algo.withoutalt.shadratiomin"] = dict_of_input.get(
"L2COMM").get_value_f("ShadowCloudRatioMin")
param_shadow["algo.withoutalt.shadratiopixnb"] = dict_of_input.get(
"L2COMM").get_value_i("ShadowCloudRatioPixNb")
param_shadow["algo.withoutalt.maxdarkening"] = dict_of_input.get(
"L2COMM").get_value_f("ProjShadDarkeningMax")
param_shadow["algo.withoutalt.minpercentile"] = float(
dict_of_input.get("L2COMM").get_value_f(
"DarkeningMinPercentile")) / 100.0
param_shadow["algo.withoutalt.seradius"] = dict_of_input.get(
"L2COMM").get_value_i("SECloudRadius")
dict_of_output["cld_l2cla"] = None
if not init_Mode:
param_shadow["l2was"] = dict_of_input.get("L2Reader").get_value(
"WASImage")
param_shadow["l2rcr"] = dict_of_input.get("L2Reader").get_value(
"RCRImage")
param_shadow["l2ndt"] = dict_of_input.get("L2Reader").get_value(
"NDTImage")
shadow_app = OtbAppHandler("CloudShadow",
param_shadow,
write_output=caching)
self._apps.add_otb_app(shadow_app)
cloud_shadow_image = shadow_app.getoutput()["shadow"]
if dict_of_input.get("Params").get("CloudMaskingKnownCloudsAltitude"):
dict_of_output["cld_l2cla"] = shadow_app.getoutput()["l2cla"]
# ------------------- Cld shadow ------------------------
# -------------------------------------------------------
# -------------------------------------------------------
# ------------------- Cld shadvar ------------------------
compute_shadvar = False
cloud_shadvar_filename = os.path.join(cloud_working,
"cloud_shadvar.tif")
if not init_Mode:
param_shadcount = {"im": cloud_shadow_image, "exclude": 1}
shadcount_app = OtbAppHandler("Stats", param_shadcount)
if shadcount_app.getoutput()["excludedcount"] > 0:
compute_shadvar = True
param_shadvar = {
"tocr":
dict_of_output.get("RayleighIPTOCR"),
"edg":
dict_of_input.get("L1Reader").get_value("IPEDGSubOutput"),
"cldall":
cloud_allnoext_image,
"cldshad":
cloud_shadow_image,
"vie":
dict_of_input.get("L1Reader").get_value("ShadowVIEImage"),
"dtm":
dict_of_input.get("DEM").ALC,
"sol":
dict_of_input.get("L1Reader").get_value("SOL1Image"),
"l2rcr":
dict_of_input.get("L2Reader").get_value("RCRImage"),
"l2was":
dict_of_input.get("L2Reader").get_value("WASImage"),
"l2ndt":
dict_of_input.get("L2Reader").get_value("NDTImage"),
"l2shad":
dict_of_input.get("L2Reader").get_value(
"VectorizedCLDSubOutput")[
constants.CLOUD_MASK_SHADOWS],
"l2shadvar":
dict_of_input.get("L2Reader").get_value(
"VectorizedCLDSubOutput")[
constants.CLOUD_MASK_SHADVAR],
"l2sto":
dict_of_input.get("L2Reader").get_value("STOImage"),
"shadbandtocr":
shadowbandtocr_idx,
"shadbandrcr":
shadowbandrcr_idx,
"viehref":
grid_ref_alt.get_VIEHRef(),
"solhref":
grid_ref_alt.get_SOLH1(),
"nodata":
dict_of_input.get("Params").get("RealL2NoData"),
"maxdarkening":
dict_of_input.get("L2COMM").get_value_f(
"ShadVarDarkeningMax"),
"minpercentile":
float(
dict_of_input.get("L2COMM").get_value_f(
"ShadVarPercentile")) / 100.0,
"maxcla":
dict_of_input.get("L2COMM").get_value_f(
"MaxCloudAltitude"),
"compositelabelratio":
dict_of_input.get("L2COMM").get_value_f(
"ShadVarCompositeLabelRatio"),
"minpixelcorrelation":
dict_of_input.get("L2COMM").get_value_f("MinPixelCorrel"),
"correlthreshold":
float(
dict_of_input.get("L2COMM").get_value_f(
"CorrelThreshold")) / 100.0,
"ncorrel":
dict_of_input.get("L2COMM").get_value_f("NCorrel"),
"stolistofdates":
dict_of_input.get("Params").get("StoListOfDates"),
"jday":
dict_of_input.get("Params").get("JDay"),
"shadvar":
cloud_shadvar_filename + ":uint8"
}
shadvar_app = OtbAppHandler("CloudShadVar",
param_shadvar,
write_output=caching)
self._apps.add_otb_app(shadvar_app)
cloud_shadvar_image = shadvar_app.getoutput()["shadvar"]
if not compute_shadvar:
cloud_shadvar_image = constant_mask
# ------------------- Cld shadvar ------------------------
# -------------------------------------------------------
# -------------------------------------------------------
# ------------------- Cld Sum build ---------------------
cloud_sum_filename = os.path.join(cloud_working, "cloud_sum.tif")
param_sum = {}
if not compute_shadvar:
param_sum = {
"il": [cloud_all_image, cloud_shadow_image],
"exp": "(im1b1 !=0) || (im2b1!=0)",
"out": cloud_sum_filename + ":uint8"
}
else:
param_sum = {
"il":
[cloud_all_image, cloud_shadow_image, cloud_shadvar_image],
"exp": "(im1b1 !=0) || (im2b1!=0) || (im3b1!=0)",
"out": cloud_sum_filename + ":uint8"
}
sum_app = OtbAppHandler("BandMath", param_sum, write_output=caching)
self._apps.add_otb_app(sum_app)
cloud_sum_image = sum_app.getoutput()["out"]
# ------------------- Cld Sum build ---------------------
# -------------------------------------------------------
# -------------------------------------------------------
# ------------------- Cld dilate masks ---------------------
caching_dilate = True
if caching_dilate:
cloud_working = dict_of_input.get(
"AppHandler").get_directory_manager().get_temporary_directory(
"CloudMaskingProc_", do_always_remove=True)
cloud_reflectance_dilated_filename = os.path.join(
cloud_working, "cloud_refl_dilated.tif")
cloud_reflvar_dilated_filename = os.path.join(
cloud_working, "cloud_reflvar_dilated.tif")
cloud_extension_dilated_filename = os.path.join(
cloud_working, "cloud_extension_dilated.tif")
cloud_all_dilated_filename = os.path.join(cloud_working,
"cloud_all_dilated.tif")
cloud_shadow_dilated_filename = os.path.join(
cloud_working, "cloud_shadow_dilated.tif")
cloud_shadvar_dilated_filename = os.path.join(
cloud_working, "cloud_shadvar_dilated.tif")
cloud_sum_dilated_filename = os.path.join(cloud_working,
"cloud_sum_dilated.tif")
cloud_alt_dilated_filename = os.path.join(cloud_working,
"cloud_alt_dilated.tif")
cloud_cirrus_dilated_filename = os.path.join(
cloud_working, "cloud_cirrus_dilated.tif")
cloud_reflectance_dilated_masked_filename = os.path.join(
cloud_working, "cloud_refl_dilated_masked.tif")
cloud_reflvar_dilated_masked_filename = os.path.join(
cloud_working, "cloud_reflvar_dilated_masked.tif")
cloud_extension_dilated_masked_filename = os.path.join(
cloud_working, "cloud_extension_dilated_masked.tif")
cloud_all_dilated_masked_filename = os.path.join(
cloud_working, "cloud_all_dilated_masked.tif")
cloud_shadow_dilated_masked_filename = os.path.join(
cloud_working, "cloud_shadow_dilated_masked.tif")
cloud_shadvar_dilated_masked_filename = os.path.join(
cloud_working, "cloud_shadvar_dilated_masked.tif")
cloud_sum_dilated_masked_filename = os.path.join(
cloud_working, "cloud_sum_dilated_masked.tif")
cloud_alt_dilated_masked_filename = os.path.join(
cloud_working, "cloud_alt_dilated_masked.tif")
cloud_cirrus_dilated_masked_filename = os.path.join(
cloud_working, "cloud_cirrus_dilated_masked.tif")
mask = dict_of_input.get("L1Reader").get_value("IPEDGSubOutput")
radius = dict_of_input.get("L2COMM").get_value_i("SECloudRadius")
cloud_reflectance_dilated_masked_app = MajaCloudMaskComputation._dilate_and_mask(
cloud_reflectance_image, cloud_reflectance_dilated_filename,
cloud_reflectance_dilated_masked_filename, mask, radius,
caching_dilate)
cloud_reflectance_dilated_masked_image = cloud_reflectance_dilated_masked_app.getoutput(
)["out"]
self._apps.add_otb_app(cloud_reflectance_dilated_masked_app)
if compute_reflvar:
cloud_reflvar_dilated_masked_app = MajaCloudMaskComputation._dilate_and_mask(
cloud_reflectvar_image, cloud_reflvar_dilated_filename,
cloud_reflvar_dilated_masked_filename, mask, radius,
caching_dilate)
cloud_reflvar_dilated_masked_image = cloud_reflvar_dilated_masked_app.getoutput(
)["out"]
self._apps.add_otb_app(cloud_reflvar_dilated_masked_app)
if compute_extension:
cloud_extension_dilated_masked_app = MajaCloudMaskComputation._dilate_and_mask(
cloud_extension_image, cloud_extension_dilated_filename,
cloud_extension_dilated_masked_filename, mask, radius,
caching_dilate)
cloud_extension_dilated_masked_image = cloud_extension_dilated_masked_app.getoutput(
)["out"]
self._apps.add_otb_app(cloud_extension_dilated_masked_app)
cloud_all_dilated_masked_app = MajaCloudMaskComputation._dilate_and_mask(
cloud_all_image, cloud_all_dilated_filename,
cloud_all_dilated_masked_filename, mask, radius, caching_dilate)
cloud_all_dilated_masked_image = cloud_all_dilated_masked_app.getoutput(
)["out"]
self._apps.add_otb_app(cloud_all_dilated_masked_app)
cloud_shadow_dilated_masked_app = MajaCloudMaskComputation._dilate_and_mask(
cloud_shadow_image, cloud_shadow_dilated_filename,
cloud_shadow_dilated_masked_filename, mask, radius, caching_dilate)
cloud_shadow_dilated_masked_image = cloud_shadow_dilated_masked_app.getoutput(
)["out"]
self._apps.add_otb_app(cloud_shadow_dilated_masked_app)
if compute_shadvar:
cloud_shadvar_dilated_masked_app = MajaCloudMaskComputation._dilate_and_mask(
cloud_shadvar_image, cloud_shadvar_dilated_filename,
cloud_shadvar_dilated_masked_filename, mask, radius,
caching_dilate)
cloud_shadvar_dilated_masked_image = cloud_shadvar_dilated_masked_app.getoutput(
)["out"]
self._apps.add_otb_app(cloud_shadvar_dilated_masked_app)
cloud_sum_dilated_masked_app = MajaCloudMaskComputation._dilate_and_mask(
cloud_sum_image, cloud_sum_dilated_filename,
cloud_sum_dilated_masked_filename, mask, radius, caching_dilate)
cloud_sum_dilated_masked_image = cloud_sum_dilated_masked_app.getoutput(
)["out"]
self._apps.add_otb_app(cloud_sum_dilated_masked_app)
cloud_alt_dilated_masked_app = MajaCloudMaskComputation._dilate_and_mask(
dict_of_output.get("CLALT_Sub"), cloud_alt_dilated_filename,
cloud_alt_dilated_masked_filename, mask, radius, caching_dilate)
cloud_alt_dilated_masked_image = cloud_alt_dilated_masked_app.getoutput(
)["out"]
self._apps.add_otb_app(cloud_alt_dilated_masked_app)
if dict_of_input.get("Params").get("CirrusBandAvailable"):
cloud_cirrus_dilated_masked_app = MajaCloudMaskComputation._dilate_and_mask(
dict_of_output.get("CirrusMask"),
cloud_cirrus_dilated_filename,
cloud_cirrus_dilated_masked_filename, mask, radius,
caching_dilate)
cloud_cirrus_dilated_masked_image = cloud_cirrus_dilated_masked_app.getoutput(
)["out"]
self._apps.add_otb_app(cloud_cirrus_dilated_masked_app)
else:
cloud_cirrus_dilated_masked_image = constant_mask
# ------------------- Cld dilate masks ---------------------
# -------------------------------------------------------
# -------------------------------------------------------
# ------------------- Register outputs ------------------
# Concatenation to generate a vector image of the cloud masks
# Concatenation of all Mono band images to the vector image
# *************************************************************************************************
# *************************************************************************************************
# ATTENTION : The order of the PushBack define the order of the data in the L2 Product
# For ChecktoolProcessorQuickLookL2::Launch method, we use index of these bands
# *************************************************************************************************
# *************************************************************************************************
# AVANT MEMORY IN CHAIN CORE ALGORITHMS - DEPRECATED
# Bit 1 - Cloud_Mask.all : summary Logical or of All cloud, extension and shadow masks
# Bit 2 - Cloud_Mask.all clouds: Logical or of All cloud masks and extension
# Bit 3 - Cloud_Mask.refl : reflectance threshold
# Bit 4 - Cloud_Mask.refl_var : reflectance variation threshold
# Bit 5 - Cloud_Mask.extension : extension of the cloud mask
# Bit 6 - Cloud_Mask.alt : stereoscopic mask => VENUS et vide pour les autres
# Bit 7 - Cloud_Mask.shadows : shadows mask from clouds within image
# Bit 8 - Cloud_Mask.shadvar: shadows mask from clouds outside image bit 1 : Cloud_Mask.all
# Bit 9 - Cirrus => L8 et S2
# APRES MEMORY IN CHAIN CORE ALGORITHMS : V 4-1-0
# Bit 1 - Cloud_Mask.all : summary Logical or of All cloud, extension and shadow masks
# Bit 2 - Cloud_Mask.all clouds: Logical or of All cloud masks and extension
# Bit 3 - Cloud_Mask.shadows : shadows mask from clouds within image
# Bit 4 - Cloud_Mask.shadvar: shadows mask from clouds outside image bit 1 : Cloud_Mask.all
# Bit 5 - Cloud_Mask.refl : reflectance threshold
# Bit 6 - Cloud_Mask.refl_var : reflectance variation threshold
# Bit 7 - Cloud_Mask.extension : extension of the cloud mask
# Bit 8 - Cloud_Mask.alt : stereoscopic mask => VENUS et vide pour les autres
# Bit 9 - Cirrus : Pour L8 et S2 et vide pour les autres
# FORMAT DISK
# Bit 1 - Cloud_Mask.all : summary Logical or of All cloud and shadow masks
# Bit 2 - Cloud_Mask.all clouds: Logical or of All cloud masks
# Bit 3 - Cloud_Mask.shadows : shadows mask from clouds within image
# Bit 4 - Cloud_Mask.shadvar: shadows mask from clouds outside image bit 1 : Cloud_Mask.all
# Bit 5 - Cloud_Mask.refl : reflectance threshold
# Bit 6 - Cloud_Mask.refl_var : reflectance variation threshold
# Bit 7 - Cloud_Mask.extension : extension of the cloud mask
# Bit 8 - VENUS : Cloud_Mask.alt : stereoscopic mask
# Bit 8 - L8 et S2 : Bit 9 - Cirrus
# Bit 8 - Vide pour les autres
cld_list = []
dict_of_output[
constants.CLOUD_MASK_ALL] = cloud_sum_dilated_masked_image
cld_list.append(dict_of_output[constants.CLOUD_MASK_ALL])
dict_of_output[
constants.CLOUD_MASK_ALL_CLOUDS] = cloud_all_dilated_masked_image
cld_list.append(dict_of_output[constants.CLOUD_MASK_ALL_CLOUDS])
dict_of_output[
constants.CLOUD_MASK_SHADOWS] = cloud_shadow_dilated_masked_image
cld_list.append(dict_of_output[constants.CLOUD_MASK_SHADOWS])
if compute_shadvar:
dict_of_output[
constants.
CLOUD_MASK_SHADVAR] = cloud_shadvar_dilated_masked_image
else:
dict_of_output[constants.CLOUD_MASK_SHADVAR] = cloud_shadvar_image
cld_list.append(dict_of_output[constants.CLOUD_MASK_SHADVAR])
dict_of_output[
constants.CLOUD_MASK_REFL] = cloud_reflectance_dilated_masked_image
cld_list.append(dict_of_output[constants.CLOUD_MASK_REFL])
if compute_reflvar:
dict_of_output[
constants.
CLOUD_MASK_REFL_VAR] = cloud_reflvar_dilated_masked_image
else:
dict_of_output[
constants.CLOUD_MASK_REFL_VAR] = cloud_reflectvar_image
cld_list.append(dict_of_output[constants.CLOUD_MASK_REFL_VAR])
if compute_extension:
dict_of_output[
constants.
CLOUD_MASK_EXTENSION] = cloud_extension_dilated_masked_image
else:
dict_of_output[
constants.CLOUD_MASK_EXTENSION] = cloud_extension_image
cld_list.append(dict_of_output[constants.CLOUD_MASK_EXTENSION])
dict_of_output[
constants.CLOUD_MASK_ALT] = cloud_alt_dilated_masked_image
cld_list.append(dict_of_output[constants.CLOUD_MASK_ALT])
dict_of_output[
constants.CLOUD_MASK_CIRRUS] = cloud_cirrus_dilated_masked_image
cld_list.append(dict_of_output[constants.CLOUD_MASK_CIRRUS])
dict_of_output["CLDList"] = cld_list
LOGGER.debug(cld_list)
class MajaScatteringCorrection(MajaModule):
"""
classdocs
"""
NAME = "ScatteringCorrection"
def __init__(self):
"""
Constructor
"""
super(MajaScatteringCorrection, self).__init__()
self._scattering_sub_app = None
self.in_keys_to_check = [
"Params.Caching", "AppHandler", "Plugin", "L1Reader", "L2COMM",
"DEM", "Params.RealL2NoData"
]
self.out_keys_to_check = ["AtmoAbsIPTOAC", "hr_lutmap", "AOT_Sub"]
self.out_keys_provided = ["TOC_sub"]
self._l2_pipeline = OtbPipelineManager()
def run(self, dict_of_input, dict_of_output):
LOGGER.info("Scattering Correction start")
scat_working = dict_of_input.get(
"AppHandler").get_directory_manager().get_temporary_directory(
"ScatteringProc_", do_always_remove=True)
l_caching = dict_of_input.get("Params").get("Caching")
l_writeL2 = dict_of_input.get("Params").get(
"WriteL2ProductToL2Resolution")
tocr_sub_image = os.path.join(scat_working, "tocr_sub.tif")
bands_definition = dict_of_input.get("Plugin").BandsDefinitions
# Coarse scattering correction
param_scattering = {
"lutmap": dict_of_output["hr_lutmap"],
"toac": dict_of_output["AtmoAbsIPTOAC"],
"aot": dict_of_output["AOT_Sub"],
"edg": dict_of_input.get("L1Reader").get_value("IPEDGSubOutput"),
"dtm": dict_of_input.get("DEM").ALC,
"l2nodata": dict_of_input.get("Params").get("RealL2NoData"),
"l2bandincoarse":
bands_definition.get_list_of_band_id_in_l2_coarse(),
"tocr": tocr_sub_image
}
self._scattering_sub_app = OtbAppHandler("ScatteringCorrection",
param_scattering,
write_output=False)
dict_of_output["TOC_sub"] = self._scattering_sub_app.getoutput(
)["tocr"]
# l2 scattering correction
tocr_list = []
if l_writeL2:
l_nbRes = len(bands_definition.ListOfL2Resolution)
for r in range(0, l_nbRes):
l_res = bands_definition.ListOfL2Resolution[r]
tocr_image = os.path.join(scat_working,
"tocr_" + l_res + ".tif")
if dict_of_input.get("Plugin").WideFieldSensor:
param_scattering["anglezone"] = dict_of_input.get(
"L1Reader").get_value("AngleZoneMaskList")[r]
param_scattering["dtm"] = dict_of_input.get("DEM").ALTList[r]
param_scattering["edg"] = dict_of_input.get(
"L1Reader").get_value("L2EDGOutputList")[r]
param_scattering["aot"] = dict_of_output["AOT_" + l_res]
param_scattering["toac"] = dict_of_output["L2TOA_" + l_res]
param_scattering["tocr"] = tocr_image
l_l2bandids = bands_definition.get_list_of_l2_coarse_band_id_associated_to_l2_band_code(
bands_definition.get_list_of_l2_band_code(l_res))
param_scattering["l2bandincoarse"] = [
str(b) for b in l_l2bandids
]
scat_app = OtbAppHandler(
"ScatteringCorrection",
param_scattering,
write_output=is_croco_on("scatteringcorrection"))
self._l2_pipeline.add_otb_app(scat_app)
dict_of_output["TOC_" +
l_res] = scat_app.getoutput().get("tocr")
tocr_list.append(dict_of_output["TOC_" + l_res])
dict_of_output["L2TOCList"] = tocr_list