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)