def make_scene(id):
raster_uri = join(raw_uri,
'4_Ortho_RGBIR/top_potsdam_{}_RGBIR.tif'.format(id))
label_uri = join(processed_uri, 'labels', 'all',
'top_potsdam_{}_RGBIR.json'.format(id))
if test:
crop_uri = join(processed_uri, 'crops',
os.path.basename(raster_uri))
save_image_crop(
raster_uri,
crop_uri,
label_uri=label_uri,
size=1000,
min_features=5)
raster_uri = crop_uri
raster_source = RasterioSourceConfig(
uris=[raster_uri], channel_order=[0, 1, 2])
label_source = ObjectDetectionLabelSourceConfig(
vector_source=GeoJSONVectorSourceConfig(
uri=label_uri, default_class_id=0, ignore_crs_field=True))
return SceneConfig(
id=id, raster_source=raster_source, label_source=label_source)
def make_scene(id):
id = id.replace('-', '_')
raster_uri = '{}/4_Ortho_RGBIR/top_potsdam_{}_RGBIR.tif'.format(
raw_uri, id)
label_uri = '{}/5_Labels_for_participants/top_potsdam_{}_label.tif'.format(
raw_uri, id)
if test:
crop_uri = join(processed_uri, 'crops',
os.path.basename(raster_uri))
label_crop_uri = join(processed_uri, 'crops',
os.path.basename(label_uri))
save_image_crop(raster_uri,
crop_uri,
label_uri=label_uri,
label_crop_uri=label_crop_uri,
size=600,
vector_labels=False)
raster_uri = crop_uri
label_uri = label_crop_uri
# infrared, red, green
channel_order = [3, 0, 1]
raster_source = RasterioSourceConfig(uris=[raster_uri],
channel_order=channel_order)
# Using with_rgb_class_map because label TIFFs have classes encoded as
# RGB colors.
label_source = SemanticSegmentationLabelSourceConfig(
rgb_class_config=class_config,
raster_source=RasterioSourceConfig(uris=[label_uri]))
# URI will be injected by scene config.
# Using rgb=True because we want prediction TIFFs to be in
# RGB format.
label_store = SemanticSegmentationLabelStoreConfig(
rgb=True, vector_output=[PolygonVectorOutputConfig(class_id=0)])
scene = SceneConfig(id=id,
raster_source=raster_source,
label_source=label_source,
label_store=label_store)
return scene
def make_scene(scene_info):
(raster_uri, label_uri) = scene_info
raster_uri = join(raw_uri, raster_uri)
label_uri = join(processed_uri, label_uri)
aoi_uri = join(raw_uri, aoi_path)
if test:
crop_uri = join(processed_uri, 'crops',
os.path.basename(raster_uri))
label_crop_uri = join(processed_uri, 'crops',
os.path.basename(label_uri))
save_image_crop(
raster_uri,
crop_uri,
label_uri=label_uri,
label_crop_uri=label_crop_uri,
size=600,
min_features=20,
class_config=class_config)
raster_uri = crop_uri
label_uri = label_crop_uri
id = os.path.splitext(os.path.basename(raster_uri))[0]
raster_source = RasterioSourceConfig(
channel_order=[0, 1, 2], uris=[raster_uri])
label_source = ChipClassificationLabelSourceConfig(
vector_source=GeoJSONVectorSourceConfig(
uri=label_uri, default_class_id=1, ignore_crs_field=True),
ioa_thresh=0.5,
use_intersection_over_cell=False,
pick_min_class_id=False,
background_class_id=0,
infer_cells=True)
return SceneConfig(
id=id,
raster_source=raster_source,
label_source=label_source,
aoi_uris=[aoi_uri])
def make_scene(scene_info):
(raster_uri, label_uri) = scene_info
raster_uri = join(raw_uri, raster_uri)
label_uri = join(processed_uri, label_uri)
if test:
crop_uri = join(processed_uri, 'crops',
os.path.basename(raster_uri))
save_image_crop(raster_uri, crop_uri, size=600, min_features=5)
raster_uri = crop_uri
id = os.path.splitext(os.path.basename(raster_uri))[0]
raster_source = RasterioSourceConfig(uris=[raster_uri],
channel_order=[0, 1, 2])
label_source = ObjectDetectionLabelSourceConfig(
vector_source=GeoJSONVectorSourceConfig(
uri=label_uri, default_class_id=0, ignore_crs_field=True))
return SceneConfig(id=id,
raster_source=raster_source,
label_source=label_source)
