def _test_class_inf(self, props, exp_class_ids, default_class_id=None):
geojson = {
'type':
'FeatureCollection',
'features': [{
'properties': props,
'geometry': {
'type': 'Point',
'coordinates': [1, 1]
}
}]
}
json_to_file(geojson, self.uri)
class_config = ClassConfig(names=['building', 'car', 'tree'])
class_id_to_filter = {
0: ['==', 'type', 'building'],
1: ['any', ['==', 'type', 'car'], ['==', 'type', 'auto']]
}
vs_cfg = GeoJSONVectorSourceConfig(
uri=self.uri,
class_id_to_filter=class_id_to_filter,
default_class_id=default_class_id)
vs = vs_cfg.build(class_config, IdentityCRSTransformer())
trans_geojson = vs.get_geojson()
class_ids = [
f['properties']['class_id'] for f in trans_geojson['features']
]
self.assertEqual(class_ids, exp_class_ids)
def transform_geojson(self,
geojson,
line_bufs=None,
point_bufs=None,
crs_transformer=None,
to_map_coords=False):
if crs_transformer is None:
crs_transformer = IdentityCRSTransformer()
class_config = ClassConfig(names=['building'])
json_to_file(geojson, self.uri)
cfg = GeoJSONVectorSourceConfig(
uri=self.uri,
line_bufs=line_bufs,
point_bufs=point_bufs,
default_class_id=0)
source = cfg.build(class_config, crs_transformer)
return source.get_geojson(to_map_coords=to_map_coords)
def save_image_crop(image_uri,
image_crop_uri,
label_uri=None,
label_crop_uri=None,
size=600,
min_features=10,
vector_labels=True,
class_config=None):
"""Save a crop of an image to use for testing.
If label_uri is set, the crop needs to cover >= min_features.
Args:
image_uri: URI of original image
image_crop_uri: URI of cropped image to save
label_uri: optional URI of label file
label_crop_uri: optional URI of cropped labels to save
size: height and width of crop
Raises:
ValueError if cannot find a crop satisfying min_features constraint.
"""
if not file_exists(image_crop_uri):
print('Saving test crop to {}...'.format(image_crop_uri))
old_environ = os.environ.copy()
try:
request_payer = S3FileSystem.get_request_payer()
if request_payer == 'requester':
os.environ['AWS_REQUEST_PAYER'] = request_payer
im_dataset = rasterio.open(image_uri)
h, w = im_dataset.height, im_dataset.width
extent = Box(0, 0, h, w)
windows = extent.get_windows(size, size)
if label_uri and vector_labels:
crs_transformer = RasterioCRSTransformer.from_dataset(
im_dataset)
geojson_vs_config = GeoJSONVectorSourceConfig(
uri=label_uri, default_class_id=0, ignore_crs_field=True)
vs = geojson_vs_config.build(class_config, crs_transformer)
geojson = vs.get_geojson()
geoms = []
for f in geojson['features']:
g = shape(f['geometry'])
geoms.append(g)
tree = STRtree(geoms)
def p2m(x, y, z=None):
return crs_transformer.pixel_to_map((x, y))
for w in windows:
use_window = True
if label_uri and vector_labels:
w_polys = tree.query(w.to_shapely())
use_window = len(w_polys) >= min_features
if use_window and label_crop_uri is not None:
print('Saving test crop labels to {}...'.format(
label_crop_uri))
label_crop_features = [
mapping(transform(p2m, wp)) for wp in w_polys
]
label_crop_json = {
'type':
'FeatureCollection',
'features': [{
'geometry': f
} for f in label_crop_features]
}
json_to_file(label_crop_json, label_crop_uri)
if use_window:
crop_image(image_uri, w, image_crop_uri)
if not vector_labels and label_uri and label_crop_uri:
crop_image(label_uri, w, label_crop_uri)
break
if not use_window:
raise ValueError('Could not find a good crop.')
finally:
os.environ.clear()
os.environ.update(old_environ)