def test_get_labels_rgb(self):
data = np.zeros((10, 10, 3), dtype=np.uint8)
data[7:, 7:, :] = [1, 1, 1]
raster_source = MockRasterSource([0, 1, 2], 3)
raster_source.set_raster(data)
rgb_class_config = ClassConfig(names=['a'], colors=['#010101'])
rgb_class_config.ensure_null_class()
label_source = SemanticSegmentationLabelSource(
raster_source=raster_source, rgb_class_config=rgb_class_config)
with label_source.activate():
window = Box.make_square(7, 7, 3)
labels = label_source.get_labels(window=window)
label_arr = labels.get_label_arr(window)
expected_label_arr = np.zeros((3, 3))
np.testing.assert_array_equal(label_arr, expected_label_arr)
def _test_class_inf(self, props, exp_class_ids, default_class_id=None):
geojson = {
'type':
'FeatureCollection',
'features': [{
'properties': props,
'geometry': {
'type': 'Point',
'coordinates': [1, 1]
}
}]
}
json_to_file(geojson, self.uri)
class_config = ClassConfig(names=['building', 'car', 'tree'])
class_id_to_filter = {
0: ['==', 'type', 'building'],
1: ['any', ['==', 'type', 'car'], ['==', 'type', 'auto']]
}
vs_cfg = GeoJSONVectorSourceConfig(
uri=self.uri,
class_id_to_filter=class_id_to_filter,
default_class_id=default_class_id)
vs = vs_cfg.build(class_config, IdentityCRSTransformer())
trans_geojson = vs.get_geojson()
class_ids = [
f['properties']['class_id'] for f in trans_geojson['features']
]
self.assertEqual(class_ids, exp_class_ids)
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 setUp(self):
self.crs_transformer = IdentityCRSTransformer()
self.extent = Box.make_square(0, 0, 10)
self.tmp_dir_obj = rv_config.get_tmp_dir()
self.tmp_dir = self.tmp_dir_obj.name
self.class_id = 0
self.background_class_id = 1
self.line_buffer = 1
self.class_config = ClassConfig(names=['a'])
self.uri = join(self.tmp_dir, 'tmp.json')
def setUp(self):
self.crs_transformer = DoubleCRSTransformer()
self.geojson = {
'type':
'FeatureCollection',
'features': [{
'type': 'Feature',
'geometry': {
'type':
'MultiPolygon',
'coordinates': [[[[0., 0.], [0., 2.], [2., 2.], [2., 0.],
[0., 0.]]]]
},
'properties': {
'class_name': 'car',
'class_id': 0,
'score': 0.0
}
}, {
'type': 'Feature',
'geometry': {
'type':
'Polygon',
'coordinates': [[[2., 2.], [2., 4.], [4., 4.], [4., 2.],
[2., 2.]]]
},
'properties': {
'score': 0.0,
'class_name': 'house',
'class_id': 1
}
}]
}
self.class_config = ClassConfig(names=['car', 'house'])
self.box1 = Box.make_square(0, 0, 4)
self.box2 = Box.make_square(4, 4, 4)
self.class_id1 = 0
self.class_id2 = 1
self.background_class_id = 2
geoms = []
for f in self.geojson['features']:
g = shape(f['geometry'])
g.class_id = f['properties']['class_id']
geoms.append(g)
self.str_tree = STRtree(geoms)
self.file_name = 'labels.json'
self.tmp_dir = rv_config.get_tmp_dir()
self.uri = os.path.join(self.tmp_dir.name, self.file_name)
json_to_file(self.geojson, self.uri)
def transform_geojson(self,
geojson,
line_bufs=None,
point_bufs=None,
crs_transformer=None,
to_map_coords=False):
if crs_transformer is None:
crs_transformer = IdentityCRSTransformer()
class_config = ClassConfig(names=['building'])
json_to_file(geojson, self.uri)
cfg = GeoJSONVectorSourceConfig(uri=self.uri,
line_bufs=line_bufs,
point_bufs=point_bufs,
default_class_id=0)
source = cfg.build(class_config, crs_transformer)
return source.get_geojson(to_map_coords=to_map_coords)
def test_compute(self):
class_config = ClassConfig(names=['one', 'two'])
class_config.update()
class_config.ensure_null_class()
gt_array = np.zeros((4, 4, 1), dtype=np.uint8)
gt_array[2, 2, 0] = 1
gt_array[0, 0, 0] = 2
gt_raster = MockRasterSource([0], 1)
gt_raster.set_raster(gt_array)
gt_label_source = SemanticSegmentationLabelSource(
raster_source=gt_raster)
p_array = np.zeros((4, 4, 1), dtype=np.uint8)
p_array[1, 1, 0] = 1
p_raster = MockRasterSource([0], 1)
p_raster.set_raster(p_array)
p_label_source = SemanticSegmentationLabelSource(
raster_source=p_raster)
eval = SemanticSegmentationEvaluation(class_config)
eval.compute(gt_label_source.get_labels(), p_label_source.get_labels())
tp0 = 16 - 3 # 4*4 - 3 true positives for class 0
fp0 = 1 # 1 false positive (2,2) and one don't care at (0,0)
fn0 = 1 # one false negative (1,1)
precision0 = float(tp0) / (tp0 + fp0)
recall0 = float(tp0) / (tp0 + fn0)
f10 = 2 * float(precision0 * recall0) / (precision0 + recall0)
tp1 = 0 # 0 true positives for class 1
fn1 = 1 # one false negative (2,2)
precision1 = 0 # float(tp1) / (tp1 + fp1) where fp1 == 1
recall1 = float(tp1) / (tp1 + fn1)
f11 = None
self.assertAlmostEqual(precision0,
eval.class_to_eval_item[0].precision)
self.assertAlmostEqual(recall0, eval.class_to_eval_item[0].recall)
self.assertAlmostEqual(f10, eval.class_to_eval_item[0].f1)
self.assertEqual(precision1, eval.class_to_eval_item[1].precision)
self.assertAlmostEqual(recall1, eval.class_to_eval_item[1].recall)
self.assertAlmostEqual(f11, eval.class_to_eval_item[1].f1)
avg_conf_mat = np.array([[0, 0, 0], [13., 1, 0], [1, 0, 0]])
avg_recall = (14 / 15) * recall0 + (1 / 15) * recall1
self.assertTrue(np.array_equal(avg_conf_mat, eval.avg_item.conf_mat))
self.assertEqual(avg_recall, eval.avg_item.recall)
def setUp(self):
self.file_name = 'labels.json'
self.tmp_dir = rv_config.get_tmp_dir()
self.file_path = os.path.join(self.tmp_dir.name, self.file_name)
self.crs_transformer = DoubleCRSTransformer()
self.geojson = {
'type':
'FeatureCollection',
'features': [{
'type': 'Feature',
'geometry': {
'type':
'Polygon',
'coordinates': [[[0., 0.], [0., 1.], [1., 1.], [1., 0.],
[0., 0.]]]
},
'properties': {
'class_id': 0,
'score': 0.9
}
}, {
'type': 'Feature',
'geometry': {
'type':
'Polygon',
'coordinates': [[[1., 1.], [1., 2.], [2., 2.], [2., 1.],
[1., 1.]]]
},
'properties': {
'score': 0.9,
'class_id': 1
}
}]
}
self.extent = Box.make_square(0, 0, 10)
self.class_config = ClassConfig(names=['car', 'house'])
json_to_file(self.geojson, self.file_path)
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 test_compute_ignore_class(self):
class_config = ClassConfig(names=['one', 'two'])
class_config.update()
class_config.ensure_null_class()
gt_array = np.zeros((4, 4, 1), dtype=np.uint8)
gt_array[0, 0, 0] = 2
gt_raster = MockRasterSource([0], 1)
gt_raster.set_raster(gt_array)
gt_label_source = SemanticSegmentationLabelSource(
raster_source=gt_raster)
pred_array = np.zeros((4, 4, 1), dtype=np.uint8)
pred_array[0, 0, 0] = 1
pred_raster = MockRasterSource([0], 1)
pred_raster.set_raster(pred_array)
pred_label_source = SemanticSegmentationLabelSource(
raster_source=pred_raster)
eval = SemanticSegmentationEvaluation(class_config)
eval.compute(gt_label_source.get_labels(),
pred_label_source.get_labels())
self.assertAlmostEqual(1.0, eval.class_to_eval_item[0].f1)
self.assertAlmostEqual(1.0, eval.avg_item.f1)
def get_config(runner, root_uri, data_uri=None, full_train=False):
def get_path(part):
if full_train:
return join(data_uri, part)
else:
return join(dirname(__file__), part)
class_config = ClassConfig(names=['car', 'building'],
colors=['blue', 'red'])
def make_scene(scene_id, img_path, label_path):
raster_source = RasterioSourceConfig(channel_order=[0, 1, 2],
uris=[img_path])
label_source = ObjectDetectionLabelSourceConfig(
vector_source=GeoJSONVectorSourceConfig(uri=label_path,
default_class_id=None))
return SceneConfig(id=scene_id,
raster_source=raster_source,
label_source=label_source)
if full_train:
model = ObjectDetectionModelConfig(backbone=Backbone.resnet18)
solver = SolverConfig(lr=1e-4,
num_epochs=300,
batch_sz=8,
one_cycle=True,
sync_interval=300)
else:
pretrained_uri = (
's3://raster-vision-lf-dev/integration_tests/object_detection/output/train/'
'last-model.pth')
model = ObjectDetectionModelConfig(backbone=Backbone.resnet18,
init_weights=pretrained_uri)
solver = SolverConfig(lr=1e-9,
num_epochs=1,
batch_sz=2,
one_cycle=True,
sync_interval=200)
backend = PyTorchObjectDetectionConfig(model=model,
solver=solver,
log_tensorboard=False,
run_tensorboard=False,
augmentors=[])
scenes = [
make_scene('od_test', get_path('scene/image.tif'),
get_path('scene/labels.json')),
make_scene('od_test-2', get_path('scene/image2.tif'),
get_path('scene/labels2.json'))
]
dataset = DatasetConfig(class_config=class_config,
train_scenes=scenes,
validation_scenes=scenes)
chip_options = ObjectDetectionChipOptions(neg_ratio=1.0, ioa_thresh=1.0)
predict_options = ObjectDetectionPredictOptions(merge_thresh=0.1,
score_thresh=0.5)
return ObjectDetectionConfig(root_uri=root_uri,
dataset=dataset,
backend=backend,
train_chip_sz=300,
predict_chip_sz=300,
chip_options=chip_options,
predict_options=predict_options)
def make_class_config(self):
return ClassConfig(names=['name', 'building'])
def get_config(runner, root_uri, data_uri=None, full_train=False):
def get_path(part):
if full_train:
return join(data_uri, part)
else:
return join(dirname(__file__), part)
class_config = ClassConfig(names=['red', 'green'], colors=['red', 'green'])
def make_scene(id, img_path, label_path):
raster_source = RasterioSourceConfig(channel_order=[0, 1, 2],
uris=[img_path])
label_source = SemanticSegmentationLabelSourceConfig(
rgb_class_config=class_config,
raster_source=RasterioSourceConfig(uris=[label_path]))
label_store = SemanticSegmentationLabelStoreConfig(
rgb=True,
vector_output=[
PolygonVectorOutputConfig(class_id=0),
BuildingVectorOutputConfig(class_id=1)
])
return SceneConfig(id=id,
raster_source=raster_source,
label_source=label_source,
label_store=label_store)
if full_train:
model = SemanticSegmentationModelConfig(backbone=Backbone.resnet50)
solver = SolverConfig(lr=1e-4,
num_epochs=300,
batch_sz=8,
one_cycle=True,
sync_interval=300)
else:
pretrained_uri = (
's3://raster-vision-lf-dev/integration_tests/semantic_segmentation/output/'
'train/last-model.pth')
model = SemanticSegmentationModelConfig(backbone=Backbone.resnet50,
init_weights=pretrained_uri)
solver = SolverConfig(lr=1e-9,
num_epochs=1,
batch_sz=2,
one_cycle=True,
sync_interval=200)
backend = PyTorchSemanticSegmentationConfig(model=model,
solver=solver,
log_tensorboard=False,
run_tensorboard=False,
augmentors=[])
scenes = [
make_scene('test-scene', get_path('scene/image.tif'),
get_path('scene/labels.tif')),
make_scene('test-scene2', get_path('scene/image2.tif'),
get_path('scene/labels2.tif'))
]
dataset = DatasetConfig(class_config=class_config,
train_scenes=scenes,
validation_scenes=scenes)
chip_options = SemanticSegmentationChipOptions(
window_method=SemanticSegmentationWindowMethod.sliding, stride=300)
return SemanticSegmentationConfig(root_uri=root_uri,
dataset=dataset,
backend=backend,
train_chip_sz=300,
predict_chip_sz=300,
chip_options=chip_options)
def get_config(runner, root_uri, data_uri=None, full_train=False):
def get_path(part):
if full_train:
return join(data_uri, part)
else:
return join(dirname(__file__), part)
class_config = ClassConfig(names=['car', 'building', 'background'],
colors=['red', 'blue', 'black'])
def make_scene(img_path, label_path):
id = basename(img_path)
label_source = ChipClassificationLabelSourceConfig(
vector_source=GeoJSONVectorSourceConfig(uri=label_path,
default_class_id=None,
ignore_crs_field=True),
ioa_thresh=0.5,
use_intersection_over_cell=False,
pick_min_class_id=True,
background_class_id=2,
infer_cells=True)
raster_source = RasterioSourceConfig(
channel_order=[0, 1, 2],
uris=[img_path],
transformers=[StatsTransformerConfig()])
return SceneConfig(id=id,
raster_source=raster_source,
label_source=label_source)
scenes = [
make_scene(get_path('scene/image.tif'), get_path('scene/labels.json')),
make_scene(get_path('scene/image2.tif'),
get_path('scene/labels2.json'))
]
dataset = DatasetConfig(class_config=class_config,
train_scenes=scenes,
validation_scenes=scenes)
if full_train:
model = ClassificationModelConfig(backbone=Backbone.resnet18)
solver = SolverConfig(lr=1e-4,
num_epochs=300,
batch_sz=8,
one_cycle=True,
sync_interval=300)
else:
pretrained_uri = (
's3://raster-vision-lf-dev/integration_tests/chip_classification/output/'
'train/last-model.pth')
model = ClassificationModelConfig(backbone=Backbone.resnet18,
init_weights=pretrained_uri)
solver = SolverConfig(lr=1e-9,
num_epochs=1,
batch_sz=2,
one_cycle=True,
sync_interval=200)
backend = PyTorchChipClassificationConfig(model=model,
solver=solver,
log_tensorboard=False,
run_tensorboard=False,
augmentors=[])
config = ChipClassificationConfig(root_uri=root_uri,
dataset=dataset,
backend=backend,
train_chip_sz=200,
predict_chip_sz=200)
return config
def setUp(self):
self.tmp_dir = rv_config.get_tmp_dir()
self.class_config = ClassConfig(names=['one', 'two'])
self.class_config.update()
self.class_config.ensure_null_class()
class TestSemanticSegmentationEvaluator(unittest.TestCase):
def setUp(self):
self.tmp_dir = rv_config.get_tmp_dir()
self.class_config = ClassConfig(names=['one', 'two'])
self.class_config.update()
self.class_config.ensure_null_class()
def tearDown(self):
self.tmp_dir.cleanup()
def get_scene(self, class_id):
# Make scene where ground truth is all set to class_id
# and predictions are set to half 0's and half 1's
scene_id = str(class_id)
rs = MockRasterSource(channel_order=[0, 1, 2], num_channels=3)
rs.set_raster(np.zeros((10, 10, 3)))
gt_rs = MockRasterSource(channel_order=[0], num_channels=1)
gt_arr = np.full((10, 10, 1), class_id)
gt_rs.set_raster(gt_arr)
gt_ls = SemanticSegmentationLabelSource(raster_source=gt_rs)
pred_rs = MockRasterSource(channel_order=[0], num_channels=1)
pred_arr = np.zeros((10, 10, 1))
pred_arr[5:10, :, :] = 1
pred_rs.set_raster(pred_arr)
pred_ls = SemanticSegmentationLabelSource(raster_source=pred_rs)
return Scene(scene_id, rs, gt_ls, pred_ls)
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 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(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), self.get_vector_scene(1)]
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.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_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)