def test_full_copy_2(self):
with TemporaryDirectory() as tmp_dir:
cat = Catalog(id='test', description='test catalog')
image_item = Item(id='Imagery',
geometry=RANDOM_GEOM,
bbox=RANDOM_BBOX,
datetime=datetime.utcnow(),
properties={})
for key in ['ortho', 'dsm']:
image_item.add_asset(
key, Asset(href='some/{}.tif'.format(key), media_type=MediaType.GEOTIFF))
label_item = LabelItem(
id='Labels',
geometry=RANDOM_GEOM,
bbox=RANDOM_BBOX,
datetime=datetime.utcnow(),
properties={},
label_description='labels',
label_type='vector',
label_properties='label',
label_classes=[LabelClasses(classes=['one', 'two'], name='label')],
label_tasks=['classification'])
label_item.add_source(image_item, assets=['ortho'])
cat.add_items([image_item, label_item])
cat.normalize_hrefs(os.path.join(tmp_dir, 'catalog-full-copy-2-source'))
cat.save(catalog_type=CatalogType.ABSOLUTE_PUBLISHED)
cat2 = cat.full_copy()
cat2.normalize_hrefs(os.path.join(tmp_dir, 'catalog-full-copy-2-dest'))
cat2.save(catalog_type=CatalogType.ABSOLUTE_PUBLISHED)
self.check_catalog(cat, 'source')
self.check_catalog(cat2, 'dest')
def test_case_3():
root_cat = Catalog(id='test3', description='test case 3 catalog', title='test case 3 title')
image_item = Item(id='imagery-item',
geometry=RANDOM_GEOM,
bbox=RANDOM_BBOX,
datetime=datetime.utcnow(),
properties={})
image_item.add_asset('ortho', Asset(href='some/geotiff.tiff', media_type=MediaType.GEOTIFF))
overviews = [LabelOverview('label', counts=[LabelCount('one', 1), LabelCount('two', 2)])]
label_item = LabelItem(id='label-items',
geometry=RANDOM_GEOM,
bbox=RANDOM_BBOX,
datetime=datetime.utcnow(),
properties={},
label_description='ML Labels',
label_type='vector',
label_properties=['label'],
label_classes=[LabelClasses(classes=['one', 'two'], name='label')],
label_tasks=['classification'],
label_methods=['manual'],
label_overviews=overviews)
label_item.add_source(image_item, assets=['ortho'])
root_cat.add_item(image_item)
root_cat.add_item(label_item)
return root_cat
def test_from_file_pre_081(self):
d = STAC_IO.read_json(self.label_example_1_uri)
d['properties']['label:property'] = d['properties']['label:properties']
d['properties'].pop('label:properties')
d['properties']['label:overview'] = d['properties']['label:overviews']
d['properties'].pop('label:overviews')
d['properties']['label:method'] = d['properties']['label:methods']
d['properties'].pop('label:methods')
d['properties']['label:task'] = d['properties']['label:tasks']
d['properties'].pop('label:tasks')
label_example_1 = LabelItem.from_dict(d)
self.assertEqual(len(label_example_1.label_tasks), 1)
def stac_object_from_dict(d, href=None, root=None):
"""Determines how to deserialize a dictionary into a STAC object.
Args:
d (dict): The dict to parse.
href (str): Optional href that is the file location of the object being
parsed.
root (Catalog or Collection): Optional root of the catalog for this object.
If provided, the root's resolved object cache can be used to search for
previously resolved instances of the STAC object.
Note: This is used internally in STAC_IO to deserialize STAC Objects.
It is in the top level __init__ in order to avoid circular dependencies.
"""
if identify_stac_object_type(d) == STACObjectType.ITEM:
collection_cache = None
if root is not None:
collection_cache = root._resolved_objects.as_collection_cache()
merge_common_properties(d, json_href=href, collection_cache=collection_cache)
info = identify_stac_object(d)
d = migrate_to_latest(d, info)
if info.object_type == STACObjectType.CATALOG:
return Catalog.from_dict(d, href=href, root=root)
if info.object_type == STACObjectType.COLLECTION:
return Collection.from_dict(d, href=href, root=root)
if info.object_type == STACObjectType.ITEMCOLLECTION:
if Extension.SINGLE_FILE_STAC in info.common_extensions:
return SingleFileSTAC.from_dict(d, href=href, root=root)
return ItemCollection.from_dict(d, href=href, root=root)
if info.object_type == STACObjectType.ITEM:
if Extension.EO in info.common_extensions:
return EOItem.from_dict(d, href=href, root=root)
if Extension.LABEL in info.common_extensions:
return LabelItem.from_dict(d, href=href, root=root)
return Item.from_dict(d, href=href, root=root)
def test_validate_label(self):
sv = SchemaValidator()
with open(self.label_example_1_uri) as f:
label_example_1_dict = json.load(f)
sv.validate_dict(label_example_1_dict, LabelItem)
with TemporaryDirectory() as tmp_dir:
cat_dir = os.path.join(tmp_dir, 'catalog')
catalog = TestCases.test_case_1()
label_item = LabelItem.from_dict(label_example_1_dict)
catalog.add_item(label_item)
catalog.normalize_and_save(cat_dir,
catalog_type=CatalogType.SELF_CONTAINED)
cat_read = Catalog.from_file(os.path.join(cat_dir, 'catalog.json'))
label_item_read = cat_read.get_item("label-example-1-label-item")
sv = SchemaValidator()
sv.validate_object(label_item_read)
def create_label_item(item):
# Assumes the GEOJSON labels are in the
# same location as the image
img_href = item.assets['ortho'].href
label_href = '{}.geojson'.format(os.path.splitext(img_href)[0])
label_item = LabelItem(
id='Labels',
geometry=item.geometry,
bbox=item.bbox,
datetime=datetime.utcnow(),
properties={},
label_description='labels',
label_type='vector',
label_properties='label',
label_classes=[LabelClasses(classes=['one', 'two'], name='label')],
label_tasks=['classification'])
label_item.add_source(item, assets=['ortho'])
label_item.add_geojson_labels(label_href)
return [item, label_item]
def test_from_file(self):
label_example_1 = LabelItem.from_file(self.label_example_1_uri)
self.assertEqual(len(label_example_1.label_overviews[0].counts), 2)