def test_get_available_collections(self):
collections = DataCollection.get_available_collections()
self._check_collection_list(collections)
config = SHConfig()
config.sh_base_url = ServiceUrl.EOCLOUD
eocloud_collections = DataCollection.get_available_collections(
config=config)
self._check_collection_list(eocloud_collections)
self.assertNotEqual(eocloud_collections, collections)
def test_define_byoc_and_batch(self):
byoc_id = '0000d273-7e89-4f00-971e-9024f89a0000'
byoc = DataCollection.define_byoc(byoc_id, name=f'MY_BYOC')
batch = DataCollection.define_batch(byoc_id, name='MY_BATCH')
self.assertEqual(byoc, DataCollection.MY_BYOC)
self.assertEqual(batch, DataCollection.MY_BATCH)
for ds in [byoc, batch]:
self.assertTrue(ds.api_id.endswith(byoc_id))
self.assertEqual(ds.collection_id, byoc_id)
with self.assertWarns(SHDeprecationWarning):
byoc2 = DataCollection(byoc_id.replace('0', '1'))
self.assertTrue(byoc, byoc2)
def __init__(self,
data_collection,
size=None,
resolution=None,
cache_folder=None,
config=None,
max_threads=None):
"""
:param data_collection: A collection of requested satellite data.
:type data_collection: DataCollection
:param size: Number of pixels in x and y dimension.
:type size: tuple(int, int)
:param resolution: Resolution in meters, passed as a single number or a tuple of two numbers -
resolution in horizontal and resolution in vertical direction.
:type resolution: float or (float, float)
:param cache_folder: Path to cache_folder. If set to None (default) requests will not be cached.
:type cache_folder: str
:param config: An instance of SHConfig defining the service
:type config: SHConfig or None
:param max_threads: Maximum threads to be used when downloading data.
:type max_threads: int
"""
if (size is None) == (resolution is None):
raise ValueError(
"Exactly one of the parameters 'size' and 'resolution' should be given."
)
self.size = size
self.resolution = resolution
self.config = config or SHConfig()
self.max_threads = max_threads
self.data_collection = DataCollection(data_collection)
self.cache_folder = cache_folder
def __init__(self,
classifier,
data_feature,
cm_size_x=None,
cm_size_y=None,
cmask_feature='CLM',
cprobs_feature=None,
instance_id=None,
data_collection=None,
image_format=MimeType.TIFF_d32f,
model_evalscript=MODEL_EVALSCRIPT,
data_source=None):
""" Constructor
If both `cm_size_x` and `cm_size_y` are `None` and `data_feature` exists, cloud detection is computed at same
resolution of `data_feature`.
:param classifier: Cloud detector classifier. This object implements a `get_cloud_probability_map` and
`get_cloud_masks` functions to generate probability maps and binary masks
:param data_feature: Name of key in eopatch.data dictionary to be used as input to the classifier. If the
`data_feature` does not exist, a new OGC request at the given cloud mask resolution is made
with layer name set to `data_feature` parameter.
:param cm_size_x: Resolution to be used for computation of cloud mask. Allowed values are number of column
pixels (WMS-request) or spatial resolution (WCS-request, e.g. '10m'). Default is `None`
:param cm_size_y: Resolution to be used for computation of cloud mask. Allowed values are number of row
pixels (WMS-request) or spatial resolution (WCS-request, e.g. '10m'). Default is `None`
:param cmask_feature: Name of key to be used for the cloud mask to add. The cloud binary mask is added to the
`eopatch.mask` attribute dictionary. Default is `'clm'`.
:param cprobs_feature: Name of key to be used for the cloud probability map to add. The cloud probability map is
added to the `eopatch.data` attribute dictionary. Default is `None`, so no cloud probability map will
be computed.
:param instance_id: Instance ID to be used for OGC request. Default is `None`
:param data_collection: Data collection to be requested by OGC service request.
:param image_format: Image format to be requested by OGC service request. Default is `MimeType.TIFF_d32f`
:param model_evalscript: CustomUrlParam defining the EVALSCRIPT to be used by OGC request. Should reflect the
request necessary for the correct functioning of the classifier. For instance, for the
`S2PixelCloudDetector` classifier, `MODEL_EVALSCRIPT` is used as it requests the required 10
bands. Default is `MODEL_EVALSCRIPT`
:param data_source: A deprecated alternative to data_collection
:type data_source: DataCollection
"""
warnings.warn(
"AddCloudMaskTask is being deprecated. Please use AddMultiCloudMaskTask instead. "
"See showcase at examples/mask/CloudMaskTask.ipynb",
SHDeprecationWarning)
data_collection = DataCollection(
handle_deprecated_data_source(data_collection, data_source)
or DataCollection.SENTINEL2_L1C)
self.classifier = classifier
self.data_feature = data_feature
self.cm_feature = cmask_feature
self.cm_size_x = cm_size_x
self.cm_size_y = cm_size_y
self.cprobs_feature = cprobs_feature
self.instance_id = instance_id
self.data_collection = data_collection
self.image_format = image_format
self.model_evalscript = model_evalscript
def test_define(self):
for _ in range(3):
data_collection = DataCollection.define('NEW',
api_id='X',
sensor_type='Sensor',
bands=('B01', ),
is_timeless=True)
self.assertEqual(data_collection, DataCollection.NEW)
with self.assertRaises(ValueError):
DataCollection.define('NEW_NEW',
api_id='X',
sensor_type='Sensor',
bands=('B01', ),
is_timeless=True)
with self.assertRaises(ValueError):
DataCollection.define('NEW', api_id='Y')
def test_define_from(self):
bands = ['B01', 'XYZ']
for _ in range(3):
data_collection = DataCollection.define_from(
DataCollection.SENTINEL5P, 'NEW_5P', api_id='X', bands=bands)
self.assertEqual(data_collection, DataCollection.NEW_5P)
self.assertEqual(data_collection.api_id, 'X')
self.assertEqual(data_collection.wfs_id,
DataCollection.SENTINEL5P.wfs_id)
self.assertEqual(data_collection.bands, tuple(bands))
def __init__(self,
layer,
feature=None,
valid_data_mask_feature='IS_DATA',
service_type=None,
data_collection=None,
size_x=None,
size_y=None,
maxcc=None,
image_format=MimeType.TIFF_d32f,
instance_id=None,
custom_url_params=None,
time_difference=None,
raise_download_errors=True,
data_source=None):
# pylint: disable=too-many-arguments
warnings.warn(
"eo-learn is deprecating OGC services for the Processing API. "
"Please use SentinelHubInputTask instead. See examples/io/ProcessingIO.ipynb for examples.",
SHDeprecationWarning)
data_collection = DataCollection(
handle_deprecated_data_source(data_collection, data_source))
self.layer = layer
self.feature_type, self.feature_name = next(
self._parse_features(layer if feature is None else feature,
default_feature_type=FeatureType.DATA)())
self.valid_data_mask_feature = self._parse_features(
valid_data_mask_feature, default_feature_type=FeatureType.MASK)
self.service_type = service_type
self.data_collection = data_collection
self.size_x = size_x
self.size_y = size_y
self.maxcc = maxcc
self.image_format = image_format
self.instance_id = instance_id
if custom_url_params is None:
custom_url_params = {}
self.custom_url_params = {
**{
CustomUrlParam.SHOWLOGO: False,
CustomUrlParam.TRANSPARENT: True
},
**custom_url_params
}
self.time_difference = time_difference
self.raise_download_errors = raise_download_errors
def test_attributes(self):
ds = DataCollection.SENTINEL3_OLCI
for attr_name in [
'api_id', 'wfs_id', 'service_url', 'bands', 'sensor_type'
]:
value = getattr(ds, attr_name)
self.assertNotEqual(value, None)
self.assertEqual(value, getattr(ds.value, attr_name))
ds = DataCollection.define('EMPTY')
for attr_name in ['api_id', 'wfs_id', 'bands']:
with self.assertRaises(ValueError):
getattr(ds, attr_name)
self.assertEqual(ds.service_url, None)
request_all_bands.save_data()
print(f'The output directory has been created and a tiff file with all 13 bands was saved into ' \
'the following structure:\n')
for folder, _, filenames in os.walk(request_all_bands.data_folder):
for filename in filenames:
print(os.path.join(folder, filename))
# Example 5: Other Data Collections
"""
The sentinelhub package supports various data collections. The previous examples
have used 'DataCollection.SENTINEL2_L1C'.
"""
print('Supported DataCollections:\n')
for collection in DataCollection.get_available_collections():
print(collection)
# try Digital Elevation Model (DEM) type:
# use FLOAT32 since the values can be negative (below sea level)
evalscript_dem = '''
//VERSION=3
function setup() {
return {
input: ["DEM"],
output: {
id: "default",
bands: 1,
sampleType: SampleType.FLOAT32
}
}
"""
Tutorial to show how to obtain data from a desired data collection using sentinelhub.
"""
import cv2
import numpy as np
import matplotlib.pyplot as plt
from sentinelhub import DataCollection
from sentinelhub import SHConfig, BBox, CRS, SentinelHubRequest, MimeType, bbox_to_dimensions
def change_brightness(img, alpha=1.0, beta=0.0):
return cv2.addWeighted(img, alpha, np.zeros(img.shape, img.dtype), 0, beta)
for collection in DataCollection.get_available_collections():
print(collection)
CLIENT_ID = '9d542985-ed95-4357-b740-37596b1d5f70'
CLIENT_SECRET = ')(/q5|#Q[[C:Oy3Is46,kpQYR28ovX9y2@,6UP@D'
config = SHConfig()
if CLIENT_ID and CLIENT_SECRET:
config.sh_client_id = CLIENT_ID
config.sh_client_secret = CLIENT_SECRET
# Columbia Glacier, Alaska
glacier_bbox = BBox([-147.8, 60.96, -146.5, 61.38], crs=CRS.WGS84)
glacier_size = (700, 466)
time_interval = '2020-07-15', '2020-07-16'
evalscript_true_color = """