def process(self, input_fc: FeatureCollection) -> FeatureCollection:
"""
Given the necessary parameters and a feature collection describing the input
datasets, runs raster sharpening on each input dataset and creates an output
feature collection.
Args:
metadata: A GeoJSON FeatureCollection describing all input datasets
Returns:
A GeoJSON FeatureCollection describing all output datasets
"""
logger.debug("Using sharpening strength: %s", self.strength)
ensure_data_directories_exist()
results: List[Feature] = []
for in_feature in input_fc.features:
(
in_feature_name,
out_feature_name,
in_feature_path,
out_feature_path,
) = get_in_out_feature_names_and_paths(in_feature)
logger.debug("Input file: %s", in_feature_name)
logger.debug("Output file: %s", out_feature_name)
self.sharpen_raster(in_feature_path, out_feature_path)
out_feature = Feature(geometry=in_feature["geometry"],
bbox=in_feature["bbox"])
out_feature["properties"] = self.get_metadata(in_feature)
set_data_path(out_feature, out_feature_name)
results.append(out_feature)
logger.debug("File %s was sharpened.", out_feature_name)
logger.debug("DONE!")
return FeatureCollection(results)
def fixture_superresolution_clip():
ensure_data_directories_exist()
return Superresolution("a.SAFE", "50.550671,26.15174,50.596161,26.19195",
True, "/tmp/output")
def safe_file():
"""
This method creats a dummy .SAFE file and also dummy output
after applying pre-processing steps with snap.
:return:
"""
# pylint: disable=too-many-locals
ensure_data_directories_exist()
# Set up the whole dummy input
input_path = Path("/tmp/input")
safe_path = input_path / "0a99c5a1-75c0-4a0d-a7dc-c2a551936be4"
if safe_path.exists():
shutil.rmtree(str(safe_path))
safe_path.mkdir()
safe_file_path = (
safe_path / "S1B_IW_GRDH_1SDV_"
"20190220T050359_20190220T050424_015025_01C12F_4EA4.SAFE")
safe_file_path.mkdir()
_location_ = os.path.realpath(
os.path.join(os.getcwd(), os.path.dirname(__file__)))
with open(os.path.join(_location_, "mock_data/data.json"), "rb") as f_p:
test_featurecollection = geojson.load(f_p)
test_feature = test_featurecollection.features[0]
manifest_path = safe_file_path / "manifest.safe"
manifest_path.write_text("")
measurement_file_path = safe_file_path / "measurement"
measurement_file_path.mkdir()
vh_file = (measurement_file_path / "s1b-iw-grd-vh-"
"20190220t050359-20190220t050424-015025-01c12f-002.tiff")
vv_file = (measurement_file_path / "s1b-iw-grd-vv-"
"20190220t050359-20190220t050424-015025-01c12f-001.tiff")
make_dummy_raster_file(vh_file)
make_dummy_raster_file(vv_file)
test_safe_file = DummySafeFile(
safe_path,
safe_file_path,
manifest_path,
measurement_file_path,
vh_file,
vv_file,
test_featurecollection,
test_feature,
)
output_file_vv_before_move = Path("/tmp/input/%s_%s.tif" %
(safe_file_path.stem, "vv"))
make_dummy_raster_file(output_file_vv_before_move)
output_file_vh_before_move = Path("/tmp/input/%s_%s.tif" %
(safe_file_path.stem, "vh"))
make_dummy_raster_file(output_file_vh_before_move)
out_path = Path("/tmp/output/0a99c5a1-75c0-4a0d-a7dc-c2a551936be4")
if out_path.exists():
shutil.rmtree(str(out_path))
out_path.mkdir()
output_file_vv = out_path / "vv.tif"
make_dummy_raster_file(output_file_vv)
output_file_vh = out_path / "vh.tif"
make_dummy_raster_file(output_file_vh)
return test_safe_file
def safe_files():
"""
This method creats two dummy .SAFE files and also dummy outputs
after applying pre-processing steps with snap.
:return:
"""
# pylint: disable=too-many-locals
ensure_data_directories_exist()
_location_ = os.path.realpath(
os.path.join(os.getcwd(), os.path.dirname(__file__)))
with open(os.path.join(_location_, "mock_data/two_data.json"),
"rb") as f_p:
test_featurecollection = geojson.load(f_p)
# Set up the whole dummy input
input_path = Path("/tmp/input")
for feature in test_featurecollection.features:
uid = feature.id
s1_id = feature.properties["identification"]["externalId"] + ".SAFE"
safe_path = input_path / uid
if safe_path.exists():
shutil.rmtree(str(safe_path))
safe_path.mkdir()
safe_file_path = safe_path / s1_id
safe_file_path.mkdir()
manifest_path = safe_file_path / "manifest.safe"
manifest_path.write_text("")
measurement_file_path = safe_file_path / "measurement"
measurement_file_path.mkdir()
vh_file = measurement_file_path / Path(
"s1b-iw-grd-vh-"
"%s-002.tiff" % s1_id.lower().replace("_", "-")[17:])
vv_file = measurement_file_path / Path(
"s1b-iw-grd-vv-"
"%s-001.tiff" % s1_id.lower().replace("_", "-")[17:])
make_dummy_raster_file(vh_file)
make_dummy_raster_file(vv_file)
test_fc = DummySafeFile(
safe_path,
safe_file_path,
manifest_path,
measurement_file_path,
vh_file,
vv_file,
test_featurecollection,
feature,
)
output_file_vv_before_move = Path("/tmp/input/%s_%s.tif" %
(safe_file_path.stem, "vv"))
make_dummy_raster_file(output_file_vv_before_move)
output_file_vh_before_move = Path("/tmp/input/%s_%s.tif" %
(safe_file_path.stem, "vh"))
make_dummy_raster_file(output_file_vh_before_move)
out_path = Path("/tmp/output/%s" % uid)
if out_path.exists():
shutil.rmtree(str(out_path))
out_path.mkdir()
output_file_vv = out_path / "vv.tif"
make_dummy_raster_file(output_file_vv)
output_file_vh = out_path / "vh.tif"
make_dummy_raster_file(output_file_vh)
return test_fc
def fixture():
ensure_data_directories_exist()
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)