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,
label_uri=label_uri,
size=600)
raster_uri = crop_uri
aoi_uri = join(raw_uri, aoi_path)
id = os.path.splitext(os.path.basename(raster_uri))[0]
background_class_id = 2
label_raster_source = rv.RasterSourceConfig.builder(rv.RASTERIZED_SOURCE) \
.with_vector_source(label_uri) \
.with_rasterizer_options(background_class_id) \
.build()
label_source = rv.LabelSourceConfig.builder(rv.SEMANTIC_SEGMENTATION) \
.with_raster_source(label_raster_source) \
.build()
return rv.SceneConfig.builder() \
.with_task(task) \
.with_id(id) \
.with_raster_source(raster_uri) \
.with_label_source(label_source) \
.with_aoi_uri(aoi_uri) \
.build()
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))
save_image_crop(raster_uri, crop_uri, label_uri=label_uri,
size=600, min_features=20)
raster_uri = crop_uri
id = os.path.splitext(os.path.basename(raster_uri))[0]
label_source = rv.LabelSourceConfig.builder(rv.CHIP_CLASSIFICATION) \
.with_uri(label_uri) \
.with_ioa_thresh(0.5) \
.with_use_intersection_over_cell(False) \
.with_pick_min_class_id(True) \
.with_background_class_id(2) \
.with_infer_cells(True) \
.build()
return rv.SceneConfig.builder() \
.with_task(task) \
.with_id(id) \
.with_raster_source(raster_uri) \
.with_label_source(label_source) \
.with_aoi_uri(aoi_uri) \
.build()
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
label_source = rv.LabelSourceConfig.builder(rv.CHIP_CLASSIFICATION) \
.with_uri(label_uri) \
.with_ioa_thresh(0.5) \
.with_use_intersection_over_cell(False) \
.with_pick_min_class_id(True) \
.with_background_class_id(2) \
.with_infer_cells(True) \
.build()
return rv.SceneConfig.builder() \
.with_id(id) \
.with_task(task) \
.with_raster_source(raster_uri, channel_order=[0, 1, 2]) \
.with_label_source(label_source) \
.build()
def make_scene(id):
image_uri = os.path.join(base_uri, '{}.jpg'.format(id))
label_uri = os.path.join(base_uri, '{}.geojson'.format(id))
# aoi_uri = join(raw_uri, aoi_path)
if test:
crop_uri = join(processed_uri, 'crops', id + '.jpg')
save_image_crop(image_uri,
crop_uri,
label_uri=label_uri,
size=600,
min_features=1)
img_uri = crop_uri
label_source = rv.LabelSourceConfig.builder(rv.CHIP_CLASSIFICATION) \
.with_uri(label_uri) \
.with_ioa_thresh(0.5) \
.with_use_intersection_over_cell(False) \
.with_pick_min_class_id(True) \
.with_background_class_id(2) \
.with_infer_cells(True) \
.build()
# .with_aoi_uri(aoi_uri) \
return rv.SceneConfig.builder() \
.with_task(task) \
.with_id(id) \
.with_raster_source(image_uri) \
.with_label_source(label_source) \
.build()
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
return rv.SceneConfig.builder() \
.with_id(id) \
.with_task(task) \
.with_raster_source(raster_uri, channel_order=[0, 1, 2]) \
.with_label_source(label_uri) \
.build()
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]
label_source = rv.LabelSourceConfig.builder(rv.OBJECT_DETECTION) \
.with_uri(label_uri) \
.build()
return rv.SceneConfig.builder() \
.with_task(task) \
.with_id(id) \
.with_raster_source(raster_uri) \
.with_label_source(label_source) \
.build()
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))
save_image_crop(raster_uri, crop_uri, size=600)
raster_uri = crop_uri
# Using with_rgb_class_map because label TIFFs have classes encoded as RGB colors.
label_source = rv.LabelSourceConfig.builder(rv.SEMANTIC_SEGMENTATION) \
.with_rgb_class_map(task.class_map) \
.with_raster_source(label_uri) \
.build()
# URI will be injected by scene config.
# Using with_rgb(True) because we want prediction TIFFs to be in RGB format.
label_store = rv.LabelStoreConfig.builder(rv.SEMANTIC_SEGMENTATION_RASTER) \
.with_rgb(True) \
.build()
scene = rv.SceneConfig.builder() \
.with_task(task) \
.with_id(id) \
.with_raster_source(raster_uri,
channel_order=channel_order) \
.with_label_source(label_source) \
.with_label_store(label_store) \
.with_stats_transformer() \
.build()
return scene