def setUp(self):
self.windows = [Box.make_square(0, 0, 10), Box.make_square(0, 10, 10)]
self.label_arr0 = np.random.choice([0, 1], (10, 10))
self.label_arr1 = np.random.choice([0, 1], (10, 10))
self.labels = SemanticSegmentationLabels()
self.labels.set_label_arr(self.windows[0], self.label_arr0)
self.labels.set_label_arr(self.windows[1], self.label_arr1)
def setUp(self):
self.windows = [
Box.make_square(0, 0, 10),
Box.make_square(0, 5, 10),
Box.make_square(0, 10, 10)
]
self.num_classes = 3
self.scores_left = make_random_scores(self.num_classes, 10, 10)
self.scores_mid = make_random_scores(self.num_classes, 10, 10)
self.scores_right = make_random_scores(self.num_classes, 10, 10)
self.scores_left = self.scores_left.astype(np.float16)
self.scores_mid = self.scores_mid.astype(np.float16)
self.scores_right = self.scores_right.astype(np.float16)
self.extent = Box(0, 0, 10, 20)
self.labels = SemanticSegmentationSmoothLabels(
extent=self.extent, num_classes=self.num_classes)
self.labels[self.windows[0]] = self.scores_left
self.labels[self.windows[1]] = self.scores_mid
self.labels[self.windows[2]] = self.scores_right
arr = np.zeros((self.num_classes, 10, 20), dtype=np.float16)
arr[..., :10] += self.scores_left
arr[..., 5:15] += self.scores_mid
arr[..., 10:] += self.scores_right
self.expected_scores = arr
hits = np.zeros((10, 20), dtype=np.uint8)
hits[..., :10] += 1
hits[..., 5:15] += 1
hits[..., 10:] += 1
self.expected_hits = hits
def setUp(self):
self.windows = [Box.make_square(0, 0, 10), Box.make_square(0, 10, 10)]
self.label_arr0 = np.random.choice([0, 1], (10, 10))
self.label_arr1 = np.random.choice([0, 1], (10, 10))
self.labels = SemanticSegmentationDiscreteLabels()
self.labels[self.windows[0]] = self.label_arr0
self.labels[self.windows[1]] = self.label_arr1
def test_get_labels_inferred(self):
extent = Box.make_square(0, 0, 8)
msg = rv.LabelSourceConfig.builder(rv.CHIP_CLASSIFICATION) \
.with_uri(self.uri) \
.with_ioa_thresh(0.5) \
.with_use_intersection_over_cell(False) \
.with_pick_min_class_id(False) \
.with_background_class_id(self.background_class_id) \
.with_infer_cells(True) \
.with_cell_size(4) \
.build().to_proto()
config = rv.LabelSourceConfig.builder(rv.CHIP_CLASSIFICATION) \
.from_proto(msg).build()
source = config.create_source(self.task_config, extent,
self.crs_transformer, self.temp_dir.name)
labels = source.get_labels()
cells = labels.get_cells()
self.assertEqual(len(cells), 4)
self.assertEqual(labels.get_cell_class_id(self.box1), self.class_id1)
self.assertEqual(labels.get_cell_class_id(self.box2), self.class_id2)
self.assertEqual(labels.get_cell_class_id(Box.make_square(0, 4, 4)),
self.background_class_id)
self.assertEqual(labels.get_cell_class_id(Box.make_square(4, 0, 4)),
self.background_class_id)
def test_get_overlapping(self):
window = Box.make_square(0, 0, 2.01)
labels = ObjectDetectionLabels.get_overlapping(self.labels, window)
labels.assert_equal(self.labels)
window = Box.make_square(0, 0, 3)
labels = ObjectDetectionLabels.get_overlapping(
self.labels, window, ioa_thresh=0.5)
npboxes = np.array([[0., 0., 2., 2.]])
class_ids = np.array([1])
scores = np.array([0.9])
expected_labels = ObjectDetectionLabels(
npboxes, class_ids, scores=scores)
labels.assert_equal(expected_labels)
window = Box.make_square(0, 0, 3)
labels = ObjectDetectionLabels.get_overlapping(
self.labels, window, ioa_thresh=0.1, clip=True)
expected_npboxes = np.array([
[0., 0., 2., 2.],
[2., 2., 3., 3.],
])
expected_labels = ObjectDetectionLabels(
expected_npboxes, self.class_ids, scores=self.scores)
labels.assert_equal(expected_labels)
def test_read_with_extent(self):
# Extent only includes the first box.
extent = Box.make_square(0, 0, 3)
store = ObjectDetectionLabelSource(self.file_path,
self.crs_transformer,
self.class_map,
extent=extent)
labels = store.get_labels()
npboxes = np.array([[0., 0., 2., 2.]])
class_ids = np.array([1])
scores = np.array([0.9])
expected_labels = ObjectDetectionLabels(npboxes,
class_ids,
scores=scores)
labels.assert_equal(expected_labels)
# Extent includes both boxes, but clips the second.
extent = Box.make_square(0, 0, 3.9)
store = ObjectDetectionLabelSource(self.file_path,
self.crs_transformer,
self.class_map,
extent=extent)
labels = store.get_labels()
npboxes = np.array([[0., 0., 2., 2.], [2., 2., 3.9, 3.9]])
class_ids = np.array([1, 2])
scores = np.array([0.9, 0.9])
expected_labels = ObjectDetectionLabels(npboxes,
class_ids,
scores=scores)
labels.assert_equal(expected_labels)
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': 1,
'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': 2
}
}]
}
self.class_map = ClassMap([ClassItem(1, 'car'), ClassItem(2, 'house')])
class MockTaskConfig():
def __init__(self, class_map):
self.class_map = class_map
self.task_config = MockTaskConfig(self.class_map)
self.box1 = Box.make_square(0, 0, 4)
self.box2 = Box.make_square(4, 4, 4)
self.class_id1 = 1
self.class_id2 = 2
self.background_class_id = 3
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.temp_dir = RVConfig.get_tmp_dir()
self.uri = os.path.join(self.temp_dir.name, self.file_name)
json_to_file(self.geojson, self.uri)
def make_ground_truth_labels(self):
size = 100
nw = Box.make_square(0, 0, size)
ne = Box.make_square(0, 200, size)
se = Box.make_square(200, 200, size)
sw = Box.make_square(200, 0, size)
npboxes = Box.to_npboxes([nw, ne, se, sw])
class_ids = np.array([1, 1, 2, 2])
return ObjectDetectionLabels(npboxes, class_ids)
def test_filter_by_aoi(self):
windows = [Box.make_square(0, 0, 2), Box.make_square(0, 2, 2)]
aoi_polygons = [Box.make_square(0, 0, 3).to_shapely()]
filt_windows = Box.filter_by_aoi(windows, aoi_polygons, within=False)
self.assertListEqual(filt_windows, windows)
filt_windows = Box.filter_by_aoi(windows, aoi_polygons, within=True)
self.assertListEqual(filt_windows, windows[0:1])
def setUp(self):
self.labels = ChipClassificationLabels()
self.cell1 = Box.make_square(0, 0, 2)
self.class_id1 = 1
self.labels.set_cell(self.cell1, self.class_id1)
self.cell2 = Box.make_square(0, 2, 2)
self.class_id2 = 2
self.labels.set_cell(self.cell2, self.class_id2)
def make_predicted_labels(self):
size = 100
# Predicted labels are only there for three of the ground truth boxes,
# and are offset by 10 pixels.
nw = Box.make_square(10, 0, size)
ne = Box.make_square(10, 200, size)
se = Box.make_square(210, 200, size)
npboxes = Box.to_npboxes([nw, ne, se])
class_ids = np.array([1, 1, 2])
scores = np.ones(class_ids.shape)
return ObjectDetectionLabels(npboxes, class_ids, scores=scores)
def test_make_buffer(self):
buffer_size = 1
max_extent = Box.make_square(0, 0, 3)
buffer_box = Box(0, 0, 3, 3)
output_buffer_box = self.box.make_buffer(buffer_size, max_extent)
self.assertEqual(output_buffer_box, buffer_box)
buffer_size = 0.5
max_extent = Box.make_square(0, 0, 5)
buffer_box = Box(0, 0, 3, 5)
output_buffer_box = self.box.make_buffer(buffer_size, max_extent)
self.assertEqual(output_buffer_box, buffer_box)
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 test_filter_by_aoi(self):
aois = [Box.make_square(0, 0, 2).to_shapely()]
filt_labels = self.labels.filter_by_aoi(aois)
npboxes = np.array([[0., 0., 2., 2.]])
class_ids = np.array([1])
scores = np.array([0.9])
exp_labels = ObjectDetectionLabels(npboxes, class_ids, scores=scores)
self.assertEqual(filt_labels, exp_labels)
aois = [Box.make_square(4, 4, 2).to_shapely()]
filt_labels = self.labels.filter_by_aoi(aois)
exp_labels = ObjectDetectionLabels.make_empty()
self.assertEqual(filt_labels, exp_labels)
def test_get_windows(self):
extent = Box(0, 0, 100, 100)
windows = list(extent.get_windows(10, 10))
self.assertEqual(len(windows), 100)
extent = Box(0, 0, 100, 100)
windows = list(extent.get_windows(10, 5))
self.assertEqual(len(windows), 400)
extent = Box(0, 0, 20, 20)
windows = set(
[window.tuple_format() for window in extent.get_windows(10, 10)])
expected_windows = [
Box.make_square(0, 0, 10),
Box.make_square(10, 0, 10),
Box.make_square(0, 10, 10),
Box.make_square(10, 10, 10)
]
expected_windows = set(
[window.tuple_format() for window in expected_windows])
self.assertSetEqual(windows, expected_windows)
extent = Box(10, 10, 20, 20)
windows = set(
[window.tuple_format() for window in extent.get_windows(6, 6)])
expected_windows = [
Box.make_square(10, 10, 6),
Box.make_square(10, 16, 6),
Box.make_square(16, 10, 6),
Box.make_square(16, 16, 6)
]
expected_windows = set(
[window.tuple_format() for window in expected_windows])
self.assertSetEqual(windows, expected_windows)
def test_filter_by_aoi(self):
aois = [Box.make_square(0, 0, 2).to_shapely()]
filt_labels = self.labels.filter_by_aoi(aois)
exp_labels = ChipClassificationLabels()
cell1 = Box.make_square(0, 0, 2)
class_id1 = 1
exp_labels.set_cell(cell1, class_id1)
self.assertEqual(filt_labels, exp_labels)
aois = [Box.make_square(4, 4, 2).to_shapely()]
filt_labels = self.labels.filter_by_aoi(aois)
exp_labels = ChipClassificationLabels()
self.assertEqual(filt_labels, exp_labels)
def setUp(self):
self.windows = [Box.make_square(0, 0, 10), Box.make_square(0, 10, 10)]
self.label_arr0 = np.random.choice([1, 2], (10, 10))
self.label_arr1 = np.random.choice([1, 2], (10, 10))
def label_fn(window):
if window == self.windows[0]:
return self.label_arr0.copy()
elif window == self.windows[1]:
return self.label_arr1.copy()
else:
raise ValueError('Unknown window: {}'.format(window))
self.label_fn = label_fn
self.labels = SemanticSegmentationLabels(self.windows, label_fn)
def __init__(self,
uris,
raster_transformers,
tmp_dir,
channel_order=None,
x_shift=0.0,
y_shift=0.0):
"""Constructor.
This RasterSource can read any file that can be opened by Rasterio/GDAL
including georeferenced formats such as GeoTIFF and non-georeferenced formats
such as JPG. See https://www.gdal.org/formats_list.html for more details.
If channel_order is None, then use non-alpha channels. This also sets any
masked or NODATA pixel values to be zeros.
Args:
channel_order: list of indices of channels to extract from raw imagery
"""
self.uris = uris
self.tmp_dir = tmp_dir
self.image_tmp_dir = None
self.image_dataset = None
self.x_shift = x_shift
self.y_shift = y_shift
self.do_shift = self.x_shift != 0.0 or self.y_shift != 0.0
num_channels = None
# Activate in order to get information out of the raster
with self.activate():
num_channels = self.image_dataset.count
if channel_order is None:
colorinterp = self.image_dataset.colorinterp
if colorinterp:
channel_order = [
i for i, color_interp in enumerate(colorinterp)
if color_interp != ColorInterp.alpha
]
else:
channel_order = list(range(0, num_channels))
self.validate_channel_order(channel_order, num_channels)
mask_flags = self.image_dataset.mask_flag_enums
self.is_masked = any(
[m for m in mask_flags if m != MaskFlags.all_valid])
self.height = self.image_dataset.height
self.width = self.image_dataset.width
# Get 1x1 chip and apply raster transformers to test dtype.
test_chip = self.get_raw_chip(Box.make_square(0, 0, 1))
test_chip = test_chip[:, :, channel_order]
for transformer in raster_transformers:
test_chip = transformer.transform(test_chip, channel_order)
self.dtype = test_chip.dtype
self._set_crs_transformer()
super().__init__(channel_order, num_channels, raster_transformers)
def test_constructor_save(self):
# Read it, write it using label_store, read it again, and compare.
extent = Box.make_square(0, 0, 10)
msg = rv.LabelStoreConfig.builder(rv.CHIP_CLASSIFICATION_GEOJSON) \
.with_uri(self.uri) \
.build().to_proto()
config = rv.LabelStoreConfig.builder(rv.CHIP_CLASSIFICATION_GEOJSON) \
.from_proto(msg).build()
label_store = config.create_store(self.task_config, extent,
self.crs_transformer,
self.temp_dir.name)
labels1 = label_store.get_labels()
new_uri = os.path.join(self.temp_dir.name, 'test_save_reload.json')
msg = rv.LabelStoreConfig.builder(rv.CHIP_CLASSIFICATION_GEOJSON) \
.with_uri(new_uri) \
.build().to_proto()
config = rv.LabelStoreConfig.builder(rv.CHIP_CLASSIFICATION_GEOJSON) \
.from_proto(msg).build()
label_store = config.create_store(self.task_config, extent,
self.crs_transformer,
self.temp_dir.name)
label_store.save(labels1)
labels2 = label_store.get_labels()
self.assertDictEqual(labels1.cell_to_class_id,
labels2.cell_to_class_id)
def test_make_eroded(self):
max_extent = Box.make_square(0, 0, 10)
box = Box(1, 1, 3, 4)
buffer_size = erosion_size = 1
eroded_box = box.make_buffer(buffer_size, max_extent) \
.make_eroded(erosion_size)
self.assertEqual(eroded_box, box)
def test_global_to_local(self):
global_npboxes = np.array([[10., 10., 12., 12.], [12., 12., 14., 14.]])
window = Box.make_square(10, 10, 10)
local_npboxes = ObjectDetectionLabels.global_to_local(
global_npboxes, window)
expected_local_npboxes = np.array([[0., 0., 2., 2.], [2., 2., 4., 4.]])
np.testing.assert_array_equal(local_npboxes, expected_local_npboxes)
def test_extend(self):
labels = ChipClassificationLabels()
cell3 = Box.make_square(0, 4, 2)
class_id3 = 1
labels.set_cell(cell3, class_id3)
self.labels.extend(labels)
cells = self.labels.get_cells()
self.assertEqual(len(cells), 3)
self.assertTrue(cell3 in cells)
def test_get_cell(self):
cell = Box.make_square(0, 2, 3)
class_id = self.labels.get_cell_class_id(cell)
self.assertEqual(class_id, None)
class_id = self.labels.get_cell_class_id(self.cell1)
self.assertEqual(class_id, self.class_id1)
class_id = self.labels.get_cell_class_id(self.cell2)
self.assertEqual(class_id, self.class_id2)
def test_get_with_aoi(self):
null_class_id = 2
aoi_polygons = [Box.make_square(5, 15, 2).to_shapely()]
exp_label_arr = np.full(self.label_arr1.shape, null_class_id)
exp_label_arr[5:7, 5:7] = self.label_arr1[5:7, 5:7]
labels = self.labels.filter_by_aoi(aoi_polygons, null_class_id)
label_arr = labels.get_label_arr(self.windows[1])
np.testing.assert_array_equal(label_arr, exp_label_arr)
self.assertEqual(1, len(labels.window_to_label_arr))
def test_infer_cell6(self):
# No boxes overlap enough, use background_class_id
cell = Box.make_square(0, 0, 10)
ioa_thresh = 0.5
use_intersection_over_cell = True
background_class_id = self.background_class_id
pick_min_class_id = False
class_id = infer_cell(cell, self.str_tree, ioa_thresh,
use_intersection_over_cell, background_class_id,
pick_min_class_id)
self.assertEqual(class_id, self.background_class_id)
def test_infer_cell3(self):
# Only box 2 is in cell, but IOA isn't high enough.
cell = Box.make_square(3, 3, 3)
ioa_thresh = 0.5
use_intersection_over_cell = False
background_class_id = None
pick_min_class_id = False
class_id = infer_cell(cell, self.str_tree, ioa_thresh,
use_intersection_over_cell, background_class_id,
pick_min_class_id)
self.assertEqual(class_id, None)
def test_infer_cell7(self):
# Cell doesn't overlap with any boxes.
cell = Box.make_square(10, 10, 1)
ioa_thresh = 0.5
use_intersection_over_cell = True
background_class_id = None
pick_min_class_id = False
class_id = infer_cell(cell, self.str_tree, ioa_thresh,
use_intersection_over_cell, background_class_id,
pick_min_class_id)
self.assertEqual(class_id, None)
def test_get_labels(self):
data = np.zeros((10, 10, 1), dtype=np.uint8)
data[7:, 7:, 0] = 1
raster_source = MockRasterSource([0, 1, 2], 3)
raster_source.set_raster(data)
label_source = SemanticSegmentationLabelSource(source=raster_source)
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.ones((3, 3))
np.testing.assert_array_equal(label_arr, expected_label_arr)
def test_infer_cell2(self):
# More of box 2 is in cell.
cell = Box.make_square(1, 1, 3)
ioa_thresh = 0.5
use_intersection_over_cell = False
background_class_id = None
pick_min_class_id = False
class_id = infer_cell(cell, self.str_tree, ioa_thresh,
use_intersection_over_cell, background_class_id,
pick_min_class_id)
self.assertEqual(class_id, self.class_id2)
def test_infer_cell8(self):
# box2 overlaps more than box1, but using pick_min_class_id, so
# picks box1.
cell = Box.make_square(1, 1, 3)
ioa_thresh = 0.5
use_intersection_over_cell = False
background_class_id = None
pick_min_class_id = True
class_id = infer_cell(cell, self.str_tree, ioa_thresh,
use_intersection_over_cell, background_class_id,
pick_min_class_id)
self.assertEqual(class_id, self.class_id2)
