def process(self, metadata: FeatureCollection, params: dict):
"""
Main wrapper method to facilitate snap processing per feature
"""
polarisations: List = params.get("polarisations", ["VV"]) or ["VV"]
self.assert_input_params()
results: List[Feature] = []
out_dict: dict = {}
for in_feature in metadata.get("features"):
coordinate = in_feature["bbox"]
self.assert_dem(coordinate)
try:
processed_graphs = self.process_snap(in_feature, polarisations)
LOGGER.info("SNAP processing is finished!")
out_feature = copy.deepcopy(in_feature)
processed_tif_uuid = out_feature.properties["up42.data_path"]
out_path = "/tmp/output/%s/" % (processed_tif_uuid)
if not os.path.exists(out_path):
os.mkdir(out_path)
for out_polarisation in processed_graphs:
# Besides the path we only need to change the capabilities
shutil.move(
("%s.tif" % out_polarisation),
("%s%s.tif" %
(out_path, out_polarisation.split("_")[-1])),
)
del out_feature["properties"]["up42.data_path"]
set_data_path(out_feature, processed_tif_uuid + ".tif")
results.append(out_feature)
out_dict[processed_tif_uuid] = {
"id": processed_tif_uuid,
"z": [i.split("_")[-1] for i in processed_graphs],
"out_path": out_path,
}
Path(__file__).parent.joinpath(
"template/"
"snap_polarimetry_graph_%s.xml" % "copy").unlink()
except WrongPolarizationError:
LOGGER.error(
"%s: some or all of the polarisations (%r) don't exist "
"in this product (%s), skipping.",
"WrongPolarizationError",
polarisations,
self.safe_file_name(in_feature),
)
continue
return FeatureCollection(results), out_dict
def process(self, metadata: FeatureCollection, params: dict):
"""
Main wrapper method to facilitate snap processing per feature
"""
polarisations: List = params.get("polarisations", ["VV"]) or ["VV"]
results: List[Feature] = []
out_path: str = ''
processed_graphs: List = []
for in_feature in metadata.get("features"):
coordinate = in_feature['bbox']
self.assert_dem(coordinate)
try:
processed_graphs = self.process_snap(in_feature, polarisations)
LOGGER.info("SNAP processing is finished!")
for out_polarisation in processed_graphs:
# Besides the path we only need to change the capabilities
out_feature = copy.deepcopy(in_feature)
processed_tif_uuid = str(uuid.uuid4())
out_path = "/tmp/output/%s/" % processed_tif_uuid
os.mkdir(out_path)
shutil.move(("%s.tif" % out_polarisation),
("%s%s.tif" % (out_path, out_polarisation)))
del out_feature["properties"][SENTINEL1_L1C_GRD]
set_capability(out_feature,
SNAP_POLARIMETRIC,
processed_tif_uuid+".tif")
results.append(out_feature)
except WrongPolarizationError:
continue
Path(__file__).parent.joinpath("template/snap_polarimetry_graph_%s.xml" % "copy").unlink()
return FeatureCollection(results), out_path, processed_graphs
def process(self, input_fc: FeatureCollection):
"""
Main wrapper method to facilitate snap processing per feature
"""
polarisations: List = self.params.polarisations or ["VV"]
self.assert_input_params()
results: List[Feature] = []
out_dict: dict = {}
for in_feature in input_fc.get("features"):
coordinate = in_feature["bbox"]
self.assert_dem(coordinate)
try:
processed_graphs = self.process_snap(in_feature, polarisations)
LOGGER.info("SNAP processing is finished!")
if not processed_graphs:
LOGGER.debug("No processed images returned, will continue")
continue
out_feature = copy.deepcopy(in_feature)
processed_tif_uuid = out_feature.properties["up42.data_path"]
out_path = f"/tmp/output/{processed_tif_uuid}/"
if not os.path.exists(out_path):
os.mkdir(out_path)
for out_polarisation in processed_graphs:
# Besides the path we only need to change the capabilities
shutil.move(
("%s.tif" % out_polarisation),
("%s%s.tif" %
(out_path, out_polarisation.split("_")[-1])),
)
del out_feature["properties"]["up42.data_path"]
set_data_path(out_feature, processed_tif_uuid + ".tif")
results.append(out_feature)
out_dict[processed_tif_uuid] = {
"id": processed_tif_uuid,
"z": [i.split("_")[-1] for i in processed_graphs],
"out_path": out_path,
}
Path(__file__).parent.joinpath(
"template/"
"snap_polarimetry_graph_%s.xml" % "copy").unlink()
except WrongPolarizationError:
LOGGER.error(
f"WrongPolarizationError: some or all of the polarisations "
f"({polarisations}) don't exist in this product "
f"({self.safe_file_name(in_feature),}), skipping.", )
continue
if not results:
raise UP42Error(
SupportedErrors.NO_OUTPUT_ERROR,
"The used input parameters don't result in any output "
"when applied to the provided input images.",
)
for out_id in out_dict: # pylint: disable=consider-using-dict-items
my_out_path = out_dict[out_id]["out_path"]
out_id_z = out_dict[out_id]["z"]
if self.params.mask is not None:
self.post_process(my_out_path, out_id_z)
self.rename_final_stack(my_out_path, out_id_z)
result_fc = FeatureCollection(results)
if self.params.clip_to_aoi:
result_fc = update_extents(result_fc)
return result_fc
def run():
query: STACQuery = load_query()
ensure_data_directories_exist()
query.set_param_if_not_exists(
"zoom_level", AWSAspectum.DEFAULT_ZOOM_LEVEL
)
output_features: List[Feature] = []
feature_id: str = str(uuid.uuid4())
out_path = f'/tmp/output/{feature_id}.tif'
logger.debug(f"File output will be {out_path}")
poly = Polygon([
[query.bbox[0], query.bbox[1]],
[query.bbox[2], query.bbox[1]],
[query.bbox[2], query.bbox[3]],
[query.bbox[0], query.bbox[3]]
])
geom = json.loads(json.dumps(mapping(poly)))
feature = Feature(
id=feature_id,
bbox=query.bbox,
geometry=geom,
properties={
'up42.data.aoiclipped': f'{feature_id}.tif'
}
)
s3 = boto3.client(
's3',
aws_access_key_id=AWSAspectum.AWS_ACCESS_KEY,
aws_secret_access_key=AWSAspectum.AWS_SECRET_ACCESS_KEY,
region_name=AWSAspectum.AWS_REGION,
)
try:
response = s3.head_object(
Bucket=AWSAspectum.BUCKET_NAME,
Key=query.file_path
)
logger.debug(
f'[FILE SIZE ON S3] - '
f'{AWSAspectum.__convert_size(response["ContentLength"])}'
)
with open(out_path, 'wb') as f:
s3.download_fileobj(
AWSAspectum.BUCKET_NAME, query.file_path, f)
output_features.append(feature)
except botocore.exceptions.ClientError as e:
if e.response['Error']['Code'] == "404":
logger.error("The object does not exist.")
else:
raise
logger.debug(
f'[FILE SIZE AFTER DOWNLOAD] - '
f'{AWSAspectum.__convert_size(os.path.getsize(out_path))}'
)
result = FeatureCollection(list(output_features))
logger.debug("Saving %s result features", len(result.get("features")))
save_metadata(result)
