def test_accounts_for_aoi(self):
class_config = ClassConfig(names=['car', 'building', 'background'])
label_source_uri = data_file_path('evaluator/cc-label-filtered.json')
label_source_cfg = ChipClassificationLabelSourceConfig(
vector_source=GeoJSONVectorSourceConfig(uri=label_source_uri,
default_class_id=None))
label_store_uri = data_file_path('evaluator/cc-label-full.json')
label_store_cfg = ChipClassificationGeoJSONStoreConfig(
uri=label_store_uri)
raster_source_uri = data_file_path('evaluator/cc-label-img-blank.tif')
raster_source_cfg = RasterioSourceConfig(uris=[raster_source_uri])
aoi_uri = data_file_path('evaluator/cc-label-aoi.json')
s = SceneConfig(id='test',
raster_source=raster_source_cfg,
label_source=label_source_cfg,
label_store=label_store_cfg,
aoi_uris=[aoi_uri])
with rv_config.get_tmp_dir() as tmp_dir:
scene = s.build(class_config, tmp_dir)
output_uri = os.path.join(tmp_dir, 'eval.json')
evaluator = ChipClassificationEvaluatorConfig(
output_uri=output_uri).build(class_config)
evaluator.process([scene], tmp_dir)
overall = file_to_json(output_uri)['overall']
for item in overall:
self.assertEqual(item['f1'], 1.0)
def test_evaluator(self):
output_uri = join(self.tmp_dir.name, 'out.json')
scenes = [self.get_scene(0), self.get_scene(1)]
evaluator = SemanticSegmentationEvaluator(self.class_config,
output_uri, None)
evaluator.process(scenes, self.tmp_dir.name)
eval_json = file_to_json(output_uri)
exp_eval_json = file_to_json(data_file_path('expected-eval.json'))
self.assertDictEqual(eval_json, exp_eval_json)
def test_gets_raw_chip(self):
img_path = data_file_path('small-rgb-tile.tif')
channel_order = [0, 1]
config = RasterioSourceConfig(uris=[img_path],
channel_order=channel_order)
source = config.build(tmp_dir=self.tmp_dir)
with source.activate():
out_chip = source.get_raw_image_array()
self.assertEqual(out_chip.shape[2], 3)
def test_vector_compute(self):
class_config = ClassConfig(names=['one', 'two'])
class_config.update()
class_config.ensure_null_class()
gt_uri = data_file_path('2-gt-polygons.geojson')
pred_uri = data_file_path('2-pred-polygons.geojson')
eval = SemanticSegmentationEvaluation(class_config)
eval.compute_vector(gt_uri, pred_uri, 'polygons', 0)
# NOTE: The two geojson files referenced above contain three
# unique geometries total, each file contains two geometries,
# and there is one geometry shared between the two.
tp = 1.0
fp = 1.0
fn = 1.0
precision = float(tp) / (tp + fp)
recall = float(tp) / (tp + fn)
self.assertAlmostEqual(precision, eval.class_to_eval_item[0].precision)
self.assertAlmostEqual(recall, eval.class_to_eval_item[0].recall)
def get_vector_scene(self, class_id, use_aoi=False):
gt_uri = data_file_path('{}-gt-polygons.geojson'.format(class_id))
pred_uri = data_file_path('{}-pred-polygons.geojson'.format(class_id))
scene_id = str(class_id)
rs = MockRasterSource(channel_order=[0, 1, 3], num_channels=3)
rs.set_raster(np.zeros((10, 10, 3)))
crs_transformer = IdentityCRSTransformer()
extent = Box.make_square(0, 0, 360)
config = RasterizedSourceConfig(
vector_source=GeoJSONVectorSourceConfig(uri=gt_uri,
default_class_id=0),
rasterizer_config=RasterizerConfig(background_class_id=1))
gt_rs = config.build(self.class_config, crs_transformer, extent)
gt_ls = SemanticSegmentationLabelSource(raster_source=gt_rs)
config = RasterizedSourceConfig(
vector_source=GeoJSONVectorSourceConfig(uri=pred_uri,
default_class_id=0),
rasterizer_config=RasterizerConfig(background_class_id=1))
pred_rs = config.build(self.class_config, crs_transformer, extent)
pred_ls = SemanticSegmentationLabelSource(raster_source=pred_rs)
pred_ls.vector_output = [
PolygonVectorOutputConfig(uri=pred_uri,
denoise=0,
class_id=class_id)
]
if use_aoi:
aoi_uri = data_file_path('{}-aoi.geojson'.format(class_id))
aoi_geojson = file_to_json(aoi_uri)
aoi_polygons = [shape(aoi_geojson['features'][0]['geometry'])]
return Scene(scene_id, rs, gt_ls, pred_ls, aoi_polygons)
return Scene(scene_id, rs, gt_ls, pred_ls)
def test_vector_evaluator_with_aoi(self):
output_uri = join(self.tmp_dir.name, 'raster-out.json')
vector_output_uri = join(self.tmp_dir.name, 'vector-out.json')
scenes = [self.get_vector_scene(0, use_aoi=True)]
evaluator = SemanticSegmentationEvaluator(self.class_config,
output_uri,
vector_output_uri)
evaluator.process(scenes, self.tmp_dir.name)
vector_eval_json = file_to_json(vector_output_uri)
exp_vector_eval_json = file_to_json(
data_file_path('expected-vector-eval-with-aoi.json'))
# NOTE: The precision and recall values found in the file
# `expected-vector-eval.json` are equal to fractions of the
# form (n-1)/n for n <= 7 which can be seen to be (and have
# been manually verified to be) correct.
self.assertDictEqual(vector_eval_json, exp_vector_eval_json)
def test_shift_x(self):
# Specially-engineered image w/ one meter per pixel resolution
# in the x direction.
img_path = data_file_path('ones.tif')
channel_order = [0]
config = RasterioSourceConfig(uris=[img_path],
channel_order=channel_order,
x_shift=1.0,
y_shift=0.0)
source = config.build(tmp_dir=self.tmp_dir)
with source.activate():
extent = source.get_extent()
data = source.get_chip(extent)
self.assertEqual(data.sum(), 2**16 - 256)
column = data[:, 255, 0]
self.assertEqual(column.sum(), 0)
def test_gets_raw_chip_from_uint16_transformed_proto(self):
img_path = data_file_path('small-uint16-tile.tif')
channel_order = [0, 1]
config = RasterioSourceConfig(uris=[img_path])
raw_rs = config.build(tmp_dir=self.tmp_dir)
stats_uri = join(self.tmp_dir, 'tmp.tif')
stats = RasterStats()
stats.compute([raw_rs])
stats.save(stats_uri)
transformer = StatsTransformerConfig(stats_uri=stats_uri)
config = RasterioSourceConfig(uris=[img_path],
channel_order=channel_order,
transformers=[transformer])
rs = config.build(tmp_dir=self.tmp_dir)
with rs.activate():
out_chip = rs.get_raw_image_array()
self.assertEqual(out_chip.shape[2], 3)
def test_get_dtype(self):
img_path = data_file_path('small-rgb-tile.tif')
config = RasterioSourceConfig(uris=[img_path])
source = config.build(tmp_dir=self.tmp_dir)
self.assertEqual(source.get_dtype(), np.uint8)