def process(self, scenes, tmp_dir):
evaluation = self.create_evaluation()
vect_evaluation = self.create_evaluation()
null_class_id = self.class_config.get_null_class_id()
for scene in scenes:
log.info('Computing evaluation for scene {}...'.format(scene.id))
label_source = scene.ground_truth_label_source
label_store = scene.prediction_label_store
with ActivateMixin.compose(label_source, label_store):
ground_truth = label_source.get_labels()
predictions = label_store.get_labels()
if scene.aoi_polygons:
# Filter labels based on AOI.
ground_truth = ground_truth.filter_by_aoi(
scene.aoi_polygons, null_class_id)
predictions = predictions.filter_by_aoi(
scene.aoi_polygons, null_class_id)
scene_evaluation = self.create_evaluation()
scene_evaluation.compute(ground_truth, predictions)
evaluation.merge(scene_evaluation, scene_id=scene.id)
if hasattr(label_source, 'raster_source') and hasattr(
label_source.raster_source, 'vector_source') and hasattr(
label_store, 'vector_output'):
gt_geojson = label_source.raster_source.vector_source.get_geojson(
)
for vo in label_store.vector_output:
pred_geojson_uri = vo.uri
mode = vo.get_mode()
class_id = vo.class_id
pred_geojson_source = GeoJSONVectorSourceConfig(
uri=pred_geojson_uri, default_class_id=class_id).build(
self.class_config,
scene.raster_source.get_crs_transformer())
pred_geojson = pred_geojson_source.get_geojson()
if scene.aoi_polygons:
gt_geojson = filter_geojson_by_aoi(
gt_geojson, scene.aoi_polygons)
pred_geojson = filter_geojson_by_aoi(
pred_geojson, scene.aoi_polygons)
vect_scene_evaluation = self.create_evaluation()
vect_scene_evaluation.compute_vector(
gt_geojson, pred_geojson, mode, class_id)
vect_evaluation.merge(
vect_scene_evaluation, scene_id=scene.id)
if not evaluation.is_empty():
evaluation.save(self.output_uri)
if not vect_evaluation.is_empty():
vect_evaluation.save(self.vector_output_uri)
def build(self, class_config, tmp_dir, use_transformers=True):
raster_source = self.raster_source.build(
tmp_dir, use_transformers=use_transformers)
crs_transformer = raster_source.get_crs_transformer()
extent = raster_source.get_extent()
label_source = (self.label_source.build(class_config, crs_transformer,
extent, tmp_dir)
if self.label_source is not None else None)
label_store = (self.label_store.build(class_config, crs_transformer,
extent, tmp_dir)
if self.label_store is not None else None)
aoi_polygons = None
if self.aoi_uris is not None:
aoi_polygons = []
for uri in self.aoi_uris:
# Set default class id to 0 to avoid deleting features. If it was
# set to None, they would all be deleted.
aoi_geojson = GeoJSONVectorSourceConfig(
uri=uri, default_class_id=0, ignore_crs_field=True).build(
class_config, crs_transformer).get_geojson()
for f in aoi_geojson['features']:
aoi_polygons.append(shape(f['geometry']))
return Scene(self.id,
raster_source,
ground_truth_label_source=label_source,
prediction_label_store=label_store,
aoi_polygons=aoi_polygons)
def get_labels(self):
vector_source = GeoJSONVectorSourceConfig(uri=self.uri,
default_class_id=None).build(
self.class_config,
self.crs_transformer)
return ObjectDetectionLabels.from_geojson(vector_source.get_geojson())
def get_labels(self):
vs = GeoJSONVectorSourceConfig(uri=self.uri, default_class_id=None)
ls = ChipClassificationLabelSourceConfig(vector_source=vs).build(
self.class_config, self.crs_transformer)
return ls.get_labels()