def _correctly_load_bbox(bbox, path, is_zipped=False):
""" Helper method for loading old version of pickled BBox object
:param bbox: BBox object which was incorrectly loaded with pickle
:type bbox: sentinelhub.BBox
:param path: Path to file where BBox object is stored
:type path: str
:param is_zipped: `True` if file is zipped and `False` otherwise
:type is_zipped: bool
:return: Correctly loaded BBox object
:rtype: sentinelhub.BBox
"""
warnings.warn(
"Bounding box of your EOPatch is saved in old format which in the future won't be supported "
"anymore. Please save bounding box again, you can overwrite the existing one",
DeprecationWarning,
stacklevel=4)
with gzip.open(path) if is_zipped else open(path, 'rb') as pickle_file:
crs_cnt = -1
for _, arg, _ in pickletools.genops(pickle_file):
if arg == 'sentinelhub.constants CRS':
crs_cnt = 2
if crs_cnt == 0:
return sentinelhub.BBox(tuple(bbox), sentinelhub.CRS(arg))
crs_cnt -= 1
raise ValueError(
'Failed to correctly load BBox object, try downgrading sentinelhub package to <=2.4.7'
)
def _parse_feature_type_value(feature_type, value):
""" Checks or parses value which will be assigned to a feature type attribute of `EOPatch`. If the value
cannot be parsed correctly it raises an error.
:raises: TypeError, ValueError
"""
if feature_type.has_dict() and isinstance(value, dict):
return value if isinstance(value, _FeatureDict) else _FeatureDict(
value, feature_type)
if feature_type is FeatureType.BBOX:
if value is None or isinstance(value, sentinelhub.BBox):
return value
if isinstance(value, (tuple, list)) and len(value) == 5:
return sentinelhub.BBox(value[:4], crs=value[4])
if feature_type is FeatureType.TIMESTAMP:
if isinstance(value, (tuple, list)):
return [
timestamp if isinstance(timestamp, datetime.date) else
dateutil.parser.parse(timestamp) for timestamp in value
]
raise TypeError('Attribute {} requires value of type {} - '
'failed to parse given value'.format(
feature_type, feature_type.type()))
def get_gridded_boundary(shapefile, cell_size=1000):
# Get its bounds from new projected
bbox = shapefile.geometry.bounds
# Get the bounding box of the area
area_bbox = sentinelhub.BBox(bbox=[(bbox['minx'], bbox['miny']),
(bbox['maxx'], bbox['maxy'])],
crs=sentinelhub.CRS.WGS84)
# Convert into UTM projection (in meters)
area_bbox = sentinelhub.geo_utils.to_utm_bbox(area_bbox)
shapefile = shapefile.to_crs(str(area_bbox.crs))
bbox = shapefile.geometry.bounds
###Build the grid
def get_shape_area(bbox, distance=cell_size):
##Would like division into 100*100 patches
# Number of vertical patch xmin - xmax
c1 = int((bbox['maxx'] - bbox['minx']) /
distance) # + int((bbox['maxx'] - bbox['minx'])%distance)
# Number of horizontal patch xmin - xmax
c2 = int((bbox['maxy'] - bbox['miny']) /
distance) # + int((bbox['maxy'] - bbox['miny'])%distance)
return ((c1, c2))
split_shape = get_shape_area(bbox, distance=cell_size)
bbox_splitter = BBoxSplitter([area_bbox.geometry], area_bbox.crs,
split_shape)
bbox_list = np.array(bbox_splitter.get_bbox_list())
info_list = np.array(bbox_splitter.get_info_list())
# Prepare info of selected EOPatches
geometry = [Polygon(bbox.get_polygon()) for bbox in bbox_list]
# idxs = [info['index'] for info in info_list]
idxs_x = [info['index_x'] for info in info_list]
idxs_y = [info['index_y'] for info in info_list]
gdf = gpd.GeoDataFrame({
'index_x': idxs_x,
'index_y': idxs_y
},
crs=shapefile.crs,
geometry=geometry)
gdf.reset_index(inplace=True)
# Get the intersection of the contours from shapefile with the grid
gdf['results'] = gdf.geometry.apply(
lambda x: shapefile.geometry.intersection(x))
# Construct a boolean associated
booleans = np.array([(1 - k.is_empty) for k in gdf.results])
gdf['check'] = booleans
valid_obs = gdf.check.astype(bool)
gdf = gdf.drop(['check'], axis=1)
return gdf[valid_obs]
def get_all_scenes(polygon, dataset, start_datetime, end_datetime):
mn = numpy.min(polygon, 0)
mx = numpy.max(polygon, 0)
search_bbox = sentinelhub.BBox(bbox=[mn[1], mn[0], mx[1], mx[0]],
crs=sentinelhub.CRS.WGS84)
start_str = start_datetime.strftime('%Y-%m-%dT%H:%M:%S')
end_str = end_datetime.strftime('%Y-%m-%dT%H:%M:%S')
search_time_interval = (start_str, end_str)
wfs_iterator = sentinelhub.WebFeatureService(
search_bbox,
search_time_interval,
data_source=sentinelhub.DataSource.SENTINEL2_L1C,
maxcc=1.0,
instance_id=INSTANCE_ID)
return {(tn + date + str(n)): (tn, date, n)
for tn, date, n in wfs_iterator.get_tiles()}