def _reproject(self, eopatch, src_raster):
"""
Reprojects the raster data from Geopedia's CRS (POP_WEB) to EOPatch's CRS.
"""
height, width = src_raster.shape
dst_raster = np.ones((height, width), dtype=self.raster_dtype)
src_bbox = transform_bbox(eopatch.bbox, CRS.POP_WEB)
src_transform = rasterio.transform.from_bounds(*src_bbox,
width=width,
height=height)
dst_bbox = eopatch.bbox
dst_transform = rasterio.transform.from_bounds(*dst_bbox,
width=width,
height=height)
rasterio.warp.reproject(
src_raster,
dst_raster,
src_transform=src_transform,
src_crs={'init': CRS.ogc_string(CRS.POP_WEB)},
src_nodata=0,
dst_transform=dst_transform,
dst_crs={'init': CRS.ogc_string(eopatch.bbox.crs)},
dst_nodata=self.no_data_val)
return dst_raster
def execute(self, eopatch, *, filename=None):
""" Execute method
:param eopatch: input EOPatch
:type eopatch: EOPatch
:param filename: filename of tiff file or None if entire path has already been specified in `folder` parameter
of task initialization.
:type filename: str or None
:return: Unchanged input EOPatch
:rtype: EOPatch
"""
feature_type, feature_name = next(self.feature(eopatch))
array = eopatch[feature_type][feature_name]
array_sub = self._get_bands_subset(array)
if feature_type.is_time_dependent():
array_sub = self._get_dates_subset(array_sub, eopatch.timestamp)
else:
# add temporal dimension
array_sub = np.expand_dims(array_sub, axis=0)
if not feature_type.is_spatial():
# add height and width dimensions
array_sub = np.expand_dims(np.expand_dims(array_sub, axis=1),
axis=1)
time_dim, height, width, band_dim = array_sub.shape
index = time_dim * band_dim
dst_transform = rasterio.transform.from_bounds(*eopatch.bbox,
width=width,
height=height)
dst_crs = {'init': CRS.ogc_string(eopatch.bbox.crs)}
image_dtype = array_sub.dtype if self.image_dtype is None else self.image_dtype
if image_dtype == np.int64:
image_dtype = np.int32
warnings.warn(
'Data from feature {} cannot be exported to tiff with dtype numpy.int64. Will export as '
'numpy.int32 instead'.format((feature_type, feature_name)))
# Write it out to a file
with rasterio.open(self._get_file_path(filename),
'w',
driver='GTiff',
width=width,
height=height,
count=index,
dtype=image_dtype,
nodata=self.no_data_value,
transform=dst_transform,
crs=dst_crs) as dst:
output_array = array_sub.astype(image_dtype)
output_array = np.moveaxis(output_array, -1,
1).reshape(index, height, width)
dst.write(output_array)
return eopatch
def generate_slo_shapefile(path):
DATA_FOLDER = os.path.join('data')
area = gpd.read_file(os.path.join(DATA_FOLDER, 'svn_buffered.geojson'))
# area = gpd.read_file(os.path.join(DATA_FOLDER, 'austria.geojson'))
# Convert CRS to UTM_33N
country_crs = CRS.UTM_33N
area = area.to_crs(crs={'init': CRS.ogc_string(country_crs)})
# Get the country's shape in polygon format
country_shape = area.geometry.values.tolist()[-1]
# Plot country
area.plot()
plt.axis('off')
# Create the splitter to obtain a list of bboxes
bbox_splitter = BBoxSplitter([country_shape], country_crs,
(25 * 3, 17 * 3))
bbox_list = np.array(bbox_splitter.get_bbox_list())
info_list = np.array(bbox_splitter.get_info_list())
path_out = path + '/shapefiles'
if not os.path.isdir(path_out):
os.makedirs(path_out)
geometry = [Polygon(bbox.get_polygon()) for bbox in bbox_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={'init': CRS.ogc_string(country_crs)},
geometry=geometry)
shapefile_name = path_out + '/slovenia.shp'
gdf.to_file(shapefile_name)
return gdf, bbox_list
def test_ogc_string(self):
crs_values = ((CRS.POP_WEB, 'EPSG:3857'), (CRS.WGS84, 'EPSG:4326'),
(CRS.UTM_33N, 'EPSG:32633'), (CRS.UTM_33S, 'EPSG:32733'))
for crs, epsg in crs_values:
with self.subTest(msg=epsg):
ogc_str = CRS.ogc_string(crs)
self.assertEqual(epsg,
ogc_str,
msg="Expected {}, got {}".format(
epsg, ogc_str))
def execute(self, eopatch, *, filename):
feature_type, feature_name = next(self.feature(eopatch))
array = eopatch[feature_type][feature_name]
array_sub = self._get_bands_subset(array)
if feature_type in [
FeatureType.DATA, FeatureType.MASK, FeatureType.SCALAR
]:
array_sub = self._get_dates_subset(array_sub, eopatch.timestamp)
if feature_type is FeatureType.SCALAR:
# add height and width dimensions
array_sub = np.expand_dims(np.expand_dims(array_sub, axis=1),
axis=1)
else:
# add temporal dimension
array_sub = np.expand_dims(array_sub, axis=0)
if feature_type is FeatureType.SCALAR_TIMELESS:
# add height and width dimensions
array_sub = np.expand_dims(np.expand_dims(array_sub, axis=1),
axis=1)
time_dim, height, width, band_dim = array_sub.shape
index = time_dim * band_dim
dst_transform = rasterio.transform.from_bounds(*eopatch.bbox,
width=width,
height=height)
dst_crs = {'init': CRS.ogc_string(eopatch.bbox.crs)}
# Write it out to a file.
with rasterio.open(os.path.join(self.folder, filename),
'w',
driver='GTiff',
width=width,
height=height,
count=index,
dtype=self.image_dtype,
nodata=self.no_data_value,
transform=dst_transform,
crs=dst_crs) as dst:
output_array = array_sub.astype(self.image_dtype)
output_array = np.moveaxis(output_array, -1,
1).reshape(index, height, width)
dst.write(output_array)
return eopatch
def execute(self, eopatch, *, filename):
array = eopatch.get_feature(self.feature_type, self.feature_name)
if self.band_count == 1:
array = array[..., 0]
dst_shape = array.shape
dst_transform = rasterio.transform.from_bounds(*eopatch.bbox, width=dst_shape[1], height=dst_shape[0])
dst_crs = {'init': CRS.ogc_string(eopatch.bbox.crs)}
# Write it out to a file.
with rasterio.open(filename, 'w', driver='GTiff',
width=dst_shape[1], height=dst_shape[0],
count=self.band_count, dtype=self.image_dtype, nodata=self.no_data_value,
transform=dst_transform, crs=dst_crs) as dst:
dst.write(array.astype(self.image_dtype), indexes=self.band_count)
return eopatch
test_patchIDs.append(patchIDs[i])
test_patchIDs = np.int64(test_patchIDs) # Is this even necessary??
#TIME INTERVAL
time_interval = ['2017-01-01',
'2017-12-31'] # time interval for the sentinelhub request
#%% Section 2
#Defining AOI and making Bboxes
country = gpd.read_file('./DK.geojson')
# Convert CRS to UTM_32N
country_crs = CRS.UTM_32N
country = country.to_crs(crs={'init': CRS.ogc_string(country_crs)})
# Get the country's shape in polygon format
country_shape = country.geometry.values.tolist()[-1]
#country.plot()
#plt.axis('off');
# Create the splitter to obtain a list of bboxes
bbox_splitter_large = BBoxSplitter([country_shape], country_crs, (45, 35))
bbox_splitter_small = BBoxSplitter([country_shape], country_crs,
(45 * 3, 35 * 3))
bbox_splitter = bbox_splitter_large
bbox_list = np.array(bbox_splitter.get_bbox_list())
info_list = np.array(bbox_splitter.get_info_list())
def execute(self, eopatch, *, filename=None):
""" Execute method
:param eopatch: input EOPatch
:type eopatch: EOPatch
:param filename: filename of tiff file or None if entire path has already been specified in `folder` parameter
of task initialization.
:type filename: str or None
:return: Unchanged input EOPatch
:rtype: EOPatch
"""
try:
feature = next(self.feature(eopatch))
except ValueError as error:
LOGGER.warning(error)
if self.fail_on_missing:
raise ValueError(error)
return eopatch
image_array = self._prepare_image_array(eopatch, feature)
channel_count, height, width = image_array.shape
src_crs = {'init': CRS.ogc_string(eopatch.bbox.crs)}
src_transform = rasterio.transform.from_bounds(*eopatch.bbox,
width=width,
height=height)
if self.crs:
dst_crs = {'init': CRS.ogc_string(self.crs)}
dst_transform, dst_width, dst_height = rasterio.warp.calculate_default_transform(
src_crs, dst_crs, width, height, *eopatch.bbox)
else:
dst_crs = src_crs
dst_transform = src_transform
dst_width, dst_height = width, height
with rasterio.open(self._get_file_path(filename, create_dir=True),
'w',
driver='GTiff',
width=dst_width,
height=dst_height,
count=channel_count,
dtype=image_array.dtype,
nodata=self.no_data_value,
transform=dst_transform,
crs=dst_crs) as dst:
if dst_crs == src_crs:
dst.write(image_array)
else:
for idx in range(channel_count):
rasterio.warp.reproject(
source=image_array[idx, ...],
destination=rasterio.band(dst, idx + 1),
src_transform=src_transform,
src_crs=src_crs,
dst_transform=dst_transform,
dst_crs=dst_crs,
resampling=rasterio.warp.Resampling.nearest)
return eopatch
