def test_can_import_points(self):
expected_dataset = Dataset.from_iterable([
DatasetItem(id=1, subset='train',
image=Image(path='1.jpg', size=(5, 5)),
annotations=[
Points([0, 0, 0, 2, 4, 1], [0, 1, 2],
label=1, group=1, id=1,
attributes={'is_crowd': False}),
Polygon([0, 0, 4, 0, 4, 4],
label=1, group=1, id=1,
attributes={'is_crowd': False}),
Points([1, 2, 3, 4, 2, 3],
group=2, id=2,
attributes={'is_crowd': False}),
Bbox(1, 2, 2, 2,
group=2, id=2,
attributes={'is_crowd': False}),
Points([1, 2, 0, 2, 4, 1],
label=0, group=3, id=3,
attributes={'is_crowd': False}),
Bbox(0, 1, 4, 1,
label=0, group=3, id=3,
attributes={'is_crowd': False}),
Points([0, 0, 1, 2, 3, 4], [0, 1, 2],
group=5, id=5,
attributes={'is_crowd': False}),
Bbox(1, 2, 2, 2,
group=5, id=5,
attributes={'is_crowd': False}),
], attributes={'id': 1}),
], categories={
AnnotationType.label: LabelCategories.from_iterable(['a', 'b']),
AnnotationType.points: PointsCategories.from_iterable(
(i, None, [[0, 1], [1, 2]]) for i in range(2)
),
})
dataset = Dataset.import_from(
osp.join(DUMMY_DATASET_DIR, 'coco_person_keypoints'), 'coco')
compare_datasets(self, expected_dataset, dataset)
def test_can_import(self):
expected_dataset = Dataset.from_iterable(
[
DatasetItem(id='1',
image=np.ones((8, 8, 3)),
annotations=[Label(0), Label(1)]),
DatasetItem(
id='2', image=np.ones(
(10, 10, 3)), annotations=[Label(0)]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(label)
for label in range(2)),
})
dataset = Dataset.import_from(DUMMY_DATASET_DIR, 'imagenet')
compare_datasets(self, expected_dataset, dataset, require_images=True)
def from_iterable(cls,
iterable: Iterable[DatasetItem],
categories: Union[Dict, List[str]] = None):
if isinstance(categories, list):
categories = {
AnnotationType.label: LabelCategories.from_iterable(categories)
}
if not categories:
categories = {}
class _extractor(Extractor):
def __iter__(self):
return iter(iterable)
def categories(self):
return categories
return cls.from_extractors(_extractor())
def test_can_save_and_load(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='1', subset='train', image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=0, group=1),
Points([3.2, 3.12, 4.11, 3.2, 2.11,
2.5, 3.5, 2.11, 3.8, 2.13], label=0, group=1),
]
),
DatasetItem(id='2', subset='train', image=np.ones((10, 10, 3)),
annotations=[
Points([4.23, 4.32, 5.34, 4.45, 3.54,
3.56, 4.52, 3.51, 4.78, 3.34], label=1, group=1),
]
),
DatasetItem(id='3', subset='train', image=np.ones((8, 8, 3)),
annotations=[Label(2, group=1)]
),
DatasetItem(id='4', subset='train', image=np.ones((10, 10, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=3, group=1),
Points([3.2, 3.12, 4.11, 3.2, 2.11,
2.5, 3.5, 2.11, 3.8, 2.13], label=3, group=1),
]
),
DatasetItem(id='a/5', subset='train', image=np.ones((8, 8, 3)),
annotations=[
Bbox(2, 2, 2, 2, group=1),
]
),
DatasetItem(id='label_0', subset='train', image=np.ones((8, 8, 3)),
),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
[('label_%s' % i, 'class_%s' % i) for i in range(5)]),
})
with TestDir() as test_dir:
VggFace2Converter.convert(source_dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'vgg_face2')
compare_datasets(self, source_dataset, parsed_dataset)
def _make_label_id_map(self):
source_labels = {
id: label.name
for id, label in enumerate(self._extractor.categories().get(
AnnotationType.label, LabelCategories()).items)
}
target_labels = {
label.name: id
for id, label in enumerate(self._categories[
AnnotationType.label].items)
}
id_mapping = {
src_id: target_labels.get(src_label, 0)
for src_id, src_label in source_labels.items()
}
def map_id(src_id):
return id_mapping.get(src_id, 0)
return map_id
def test_can_import(self):
expected_dataset = Dataset.from_iterable(
[
DatasetItem(id=1,
subset='train',
image=np.ones((10, 15, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(3, 3, 2, 3, label=4),
]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(i)
for i in range(10)),
})
dataset = Dataset.import_from(DUMMY_DATASET_DIR, 'yolo')
compare_datasets(self, expected_dataset, dataset)
def test_can_import(self):
expected_dataset = Dataset.from_iterable([
DatasetItem(id=1,
image=np.ones((16, 16, 3)),
annotations=[
Bbox(0, 4, 4, 8, label=2, attributes={
'occluded': False,
'visibility': 1.0,
'ignored': False,
}),
]
),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_' + str(label) for label in range(10)),
})
dataset = Dataset.import_from(DUMMY_DATASET_DIR, 'mot_seq')
compare_datasets(self, expected_dataset, dataset)
def test_can_save_bboxes(self):
test_dataset = Dataset.from_iterable([
DatasetItem(id=1, subset='train',
image=np.ones((16, 16, 3)),
annotations=[
Bbox(0, 4, 4, 8, label=2),
Bbox(0, 4, 4, 4, label=3),
Bbox(2, 4, 4, 4),
], attributes={'source_id': ''}
),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_' + str(label) for label in range(10)),
})
with TestDir() as test_dir:
self._test_save_and_load(
test_dataset,
partial(TfDetectionApiConverter.convert, save_images=True),
test_dir)
def test_relative_paths(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='1', subset='train',
image=np.ones((4, 2, 3))),
DatasetItem(id='subdir1/1', subset='train',
image=np.ones((2, 6, 3))),
DatasetItem(id='subdir2/1', subset='train',
image=np.ones((5, 4, 3))),
], categories={
AnnotationType.label: LabelCategories(),
})
for save_images in {True, False}:
with self.subTest(save_images=save_images):
with TestDir() as test_dir:
YoloConverter.convert(source_dataset, test_dir,
save_images=save_images)
parsed_dataset = YoloImporter()(test_dir).make_dataset()
compare_datasets(self, source_dataset, parsed_dataset)
def test_can_load_dataset_with_exact_image_info(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id=1, subset='train',
image=Image(path='1.jpg', size=(10, 15)),
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(3, 3, 2, 3, label=4),
]),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_' + str(i) for i in range(10)),
})
with TestDir() as test_dir:
YoloConverter.convert(source_dataset, test_dir)
parsed_dataset = YoloImporter()(test_dir,
image_info={'1': (10, 15)}).make_dataset()
compare_datasets(self, source_dataset, parsed_dataset)
def categories(self):
label_cat = LabelCategories()
label_cat.add('label0')
label_cat.add('label9')
label_cat.add('label4')
mask_cat = MaskCategories(colormap={
k: v for k, v in mask_tools.generate_colormap(5).items()
if k in { 0, 1, 3, 4 }
})
return {
AnnotationType.label: label_cat,
AnnotationType.mask: mask_cat,
}
def test_can_save_and_load_with_multiple_labels(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id='1',
image=np.ones((8, 8, 3)),
annotations=[Label(0), Label(1)]),
DatasetItem(id='2',
image=np.ones((10, 10, 3)),
annotations=[Label(0), Label(1)]),
DatasetItem(id='3',
image=np.ones((10, 10, 3)),
annotations=[Label(0), Label(2)]),
DatasetItem(id='4',
image=np.ones((8, 8, 3)),
annotations=[Label(2), Label(4)]),
DatasetItem(id='5',
image=np.ones((10, 10, 3)),
annotations=[Label(3), Label(4)]),
DatasetItem(
id='6',
image=np.ones((10, 10, 3)),
),
DatasetItem(id='7', image=np.ones((8, 8, 3))),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(label)
for label in range(5)),
})
with TestDir() as test_dir:
ImagenetConverter.convert(source_dataset,
test_dir,
save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'imagenet')
compare_datasets(self,
source_dataset,
parsed_dataset,
require_images=True)
def test_can_save_dataset_with_image_info(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id=1, subset='train',
image=Image(path='1.jpg', size=(10, 15)),
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(3, 3, 2, 3, label=4),
]),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_' + str(i) for i in range(10)),
})
with TestDir() as test_dir:
YoloConverter.convert(source_dataset, test_dir)
save_image(osp.join(test_dir, 'obj_train_data', '1.jpg'),
np.ones((10, 15, 3))) # put the image for dataset
parsed_dataset = YoloImporter()(test_dir).make_dataset()
compare_datasets(self, source_dataset, parsed_dataset)
def __call__(self, extractor, save_dir):
os.makedirs(save_dir, exist_ok=True)
label_categories = extractor.categories().get(AnnotationType.label,
LabelCategories())
get_label = lambda label_id: label_categories.items[label_id].name \
if label_id is not None else ''
label_ids = OrderedDict((label.name, 1 + idx)
for idx, label in enumerate(label_categories.items))
map_label_id = lambda label_id: label_ids.get(get_label(label_id), 0)
self._get_label = get_label
self._get_label_id = map_label_id
subsets = extractor.subsets()
if len(subsets) == 0:
subsets = [ None ]
for subset_name in subsets:
if subset_name:
subset = extractor.get_subset(subset_name)
else:
subset_name = DEFAULT_SUBSET_NAME
subset = extractor
labelmap_path = osp.join(save_dir, DetectionApiPath.LABELMAP_FILE)
with codecs.open(labelmap_path, 'w', encoding='utf8') as f:
for label, idx in label_ids.items():
f.write(
'item {\n' +
('\tid: %s\n' % (idx)) +
("\tname: '%s'\n" % (label)) +
'}\n\n'
)
anno_path = osp.join(save_dir, '%s.tfrecord' % (subset_name))
with tf.io.TFRecordWriter(anno_path) as writer:
for item in subset:
tf_example = self._make_tf_example(item)
writer.write(tf_example.SerializeToString())
def test_can_save_dataset_with_cyrillic_and_spaces_in_filename(self):
dataset = Dataset.from_iterable(
[
DatasetItem(id="кириллица с пробелом",
image=np.ones((8, 8, 3)),
annotations=[Label(0), Label(1)]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(label)
for label in range(2)),
})
with TestDir() as test_dir:
ImagenetTxtConverter.convert(dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'imagenet_txt')
compare_datasets(self,
dataset,
parsed_dataset,
require_images=True)
def _load_categories(self, label_map_source):
if label_map_source == LabelmapType.camvid.name:
# use the default Camvid colormap
label_map = CamvidLabelMap
elif label_map_source == LabelmapType.source.name and \
AnnotationType.mask not in self._extractor.categories():
# generate colormap for input labels
labels = self._extractor.categories() \
.get(AnnotationType.label, LabelCategories())
label_map = OrderedDict((item.name, None) for item in labels.items)
elif label_map_source == LabelmapType.source.name and \
AnnotationType.mask in self._extractor.categories():
# use source colormap
labels = self._extractor.categories()[AnnotationType.label]
colors = self._extractor.categories()[AnnotationType.mask]
label_map = OrderedDict()
for idx, item in enumerate(labels.items):
color = colors.colormap.get(idx)
if color is not None:
label_map[item.name] = color
elif isinstance(label_map_source, dict):
label_map = OrderedDict(
sorted(label_map_source.items(), key=lambda e: e[0]))
elif isinstance(label_map_source,
str) and osp.isfile(label_map_source):
label_map = parse_label_map(label_map_source)
else:
raise Exception("Wrong labelmap specified, "
"expected one of %s or a file path" % \
', '.join(t.name for t in LabelmapType))
self._categories = make_camvid_categories(label_map)
self._label_map = label_map
self._label_id_mapping = self._make_label_id_map()
def test_can_import(self):
expected_dataset = Dataset.from_iterable([
DatasetItem(id='1', subset='train', image=np.zeros((8, 6, 3)),
annotations=[Label(0)]
),
DatasetItem(id='2', subset='train', image=np.zeros((2, 8, 3)),
annotations=[Label(5)]
),
DatasetItem(id='3', subset='train',
annotations=[Label(3)]
),
DatasetItem(id='4', subset='train',
annotations=[Label(5)]
),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_%s' % label for label in range(10)),
})
dataset = Dataset.import_from(DUMMY_DATASET_DIR, 'imagenet_txt')
compare_datasets(self, expected_dataset, dataset, require_images=True)
def test_can_save_and_load(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id=1,
subset='train',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(0, 1, 2, 3, label=4),
]),
DatasetItem(id=2,
subset='train',
image=np.ones((10, 10, 3)),
annotations=[
Bbox(0, 2, 4, 2, label=2),
Bbox(3, 3, 2, 3, label=4),
Bbox(2, 1, 2, 3, label=4),
]),
DatasetItem(id=3,
subset='valid',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(0, 1, 5, 2, label=2),
Bbox(0, 2, 3, 2, label=5),
Bbox(0, 2, 4, 2, label=6),
Bbox(0, 7, 3, 2, label=7),
]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(i)
for i in range(10)),
})
with TestDir() as test_dir:
YoloConverter.convert(source_dataset, test_dir, save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'yolo')
compare_datasets(self, source_dataset, parsed_dataset)
def _make_label_id_map(self):
source_labels = {
id: label.name
for id, label in enumerate(self._extractor.categories().get(
AnnotationType.label, LabelCategories()).items)
}
target_labels = {
label.name: id
for id, label in enumerate(self._categories[
AnnotationType.label].items)
}
id_mapping = {
src_id: target_labels.get(src_label, 0)
for src_id, src_label in source_labels.items()
}
void_labels = [
src_label for src_id, src_label in source_labels.items()
if src_label not in target_labels
]
if void_labels:
log.warning("The following labels are remapped to background: %s" %
', '.join(void_labels))
log.debug("Saving segmentations with the following label mapping: \n%s" %
'\n'.join(["#%s '%s' -> #%s '%s'" %
(
src_id, src_label, id_mapping[src_id],
self._categories[AnnotationType.label] \
.items[id_mapping[src_id]].name
)
for src_id, src_label in source_labels.items()
])
)
def map_id(src_id):
return id_mapping.get(src_id, 0)
return map_id
def _load_categories(self):
self._categories[AnnotationType.label] = LabelCategories()
label_cat = LabelCategories()
path = osp.join(self._dataset_dir, WiderFacePath.LABELS_FILE)
if osp.isfile(path):
with open(path, encoding='utf-8') as labels_file:
labels = [s.strip() for s in labels_file]
for label in labels:
label_cat.add(label)
else:
subset_path = osp.join(self._dataset_dir,
WiderFacePath.SUBSET_DIR + self._subset,
WiderFacePath.IMAGES_DIR)
if osp.isdir(subset_path):
for images_dir in sorted(os.listdir(subset_path)):
if osp.isdir(osp.join(subset_path, images_dir)) and \
images_dir != WiderFacePath.IMAGES_DIR_NO_LABEL:
if '--' in images_dir:
images_dir = images_dir.split('--')[1]
label_cat.add(images_dir)
return { AnnotationType.label: label_cat }
def test_can_save_masks(self):
test_dataset = Dataset.from_iterable([
DatasetItem(id=1, subset='train', image=np.ones((4, 5, 3)),
annotations=[
Mask(image=np.array([
[1, 0, 0, 1],
[0, 1, 1, 0],
[0, 1, 1, 0],
[1, 0, 0, 1],
]), label=1),
],
attributes={'source_id': ''}
),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_' + str(label) for label in range(10)),
})
with TestDir() as test_dir:
self._test_save_and_load(
test_dataset,
partial(TfDetectionApiConverter.convert, save_masks=True),
test_dir)
def test_can_save_dataset_with_unknown_image_formats(self):
test_dataset = Dataset.from_iterable([
DatasetItem(id=1,
image=ByteImage(data=encode_image(
np.ones((5, 4, 3)), 'png'),
path='1/q.e'),
attributes={'source_id': ''}),
DatasetItem(id=2,
image=ByteImage(data=encode_image(
np.ones((6, 4, 3)), 'png'),
ext='qwe'),
attributes={'source_id': ''})
],
categories={
AnnotationType.label:
LabelCategories(),
})
with TestDir() as test_dir:
self._test_save_and_load(
test_dataset,
partial(TfDetectionApiConverter.convert, save_images=True),
test_dir)
def test_can_save_and_load_with_multiple_labels(self):
source_dataset = Dataset.from_iterable([
DatasetItem(id='1', subset='train',
annotations=[Label(1), Label(3)]
),
DatasetItem(id='2', subset='train', image=np.zeros((8, 6, 3)),
annotations=[Label(0)]
),
DatasetItem(id='3', subset='train', image=np.zeros((2, 8, 3)),
),
], categories={
AnnotationType.label: LabelCategories.from_iterable(
'label_' + str(label) for label in range(10)),
})
with TestDir() as test_dir:
ImagenetTxtConverter.convert(source_dataset, test_dir,
save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'imagenet_txt')
compare_datasets(self, source_dataset, parsed_dataset,
require_images=True)
def from_iterable(cls,
iterable: Iterable[DatasetItem],
categories: Union[CategoriesInfo, List[str]] = None,
env: Environment = None):
if isinstance(categories, list):
categories = {
AnnotationType.label: LabelCategories.from_iterable(categories)
}
if not categories:
categories = {}
class _extractor(Extractor):
def __init__(self):
super().__init__(length=len(iterable) \
if hasattr(iterable, '__len__') else None)
def __iter__(self):
return iter(iterable)
def categories(self):
return categories
return cls.from_extractors(_extractor(), env=env)
def test_can_save_dataset_with_no_subsets(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id='a/b/c',
image=np.zeros((8, 4, 3)),
annotations=[Label(1)]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(label)
for label in range(10)),
})
with TestDir() as test_dir:
ImagenetTxtConverter.convert(source_dataset,
test_dir,
save_images=True)
parsed_dataset = Dataset.import_from(test_dir, 'imagenet_txt')
compare_datasets(self,
source_dataset,
parsed_dataset,
require_images=True)
def _load_categories(self):
label_cat = LabelCategories()
path = osp.join(self._dataset_dir, VggFace2Path.LABELS_FILE)
if osp.isfile(path):
with open(path, encoding='utf-8') as labels_file:
lines = [s.strip() for s in labels_file]
for line in lines:
objects = line.split()
label = objects[0]
class_name = None
if 1 < len(objects):
class_name = objects[1]
label_cat.add(label, parent=class_name)
else:
subset_path = osp.join(self._dataset_dir, self._subset)
if osp.isdir(subset_path):
for images_dir in sorted(os.listdir(subset_path)):
if osp.isdir(osp.join(subset_path, images_dir)) and \
images_dir != VggFace2Path.IMAGES_DIR_NO_LABEL:
label_cat.add(images_dir)
return {AnnotationType.label: label_cat}
def test_can_save_and_load_keypoints(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(
id=1,
subset='train',
image=np.zeros((5, 5, 3)),
annotations=[
# Full instance annotations: polygon + keypoints
Points([0, 0, 0, 2, 4, 1], [0, 1, 2],
label=3,
group=1,
id=1),
Polygon([0, 0, 4, 0, 4, 4], label=3, group=1, id=1),
# Full instance annotations: bbox + keypoints
Points([1, 2, 3, 4, 2, 3], group=2, id=2),
Bbox(1, 2, 2, 2, group=2, id=2),
# Solitary keypoints
Points([1, 2, 0, 2, 4, 1], label=5, id=3),
# Some other solitary annotations (bug #1387)
Polygon([0, 0, 4, 0, 4, 4], label=3, id=4),
# Solitary keypoints with no label
Points([0, 0, 1, 2, 3, 4], [0, 1, 2], id=5),
]),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable(str(i) for i in range(10)),
AnnotationType.points:
PointsCategories.from_iterable(
(i, None, [[0, 1], [1, 2]]) for i in range(10)),
})
target_dataset = Dataset.from_iterable(
[
DatasetItem(id=1,
subset='train',
image=np.zeros((5, 5, 3)),
annotations=[
Points([0, 0, 0, 2, 4, 1], [0, 1, 2],
label=3,
group=1,
id=1,
attributes={'is_crowd': False}),
Polygon([0, 0, 4, 0, 4, 4],
label=3,
group=1,
id=1,
attributes={'is_crowd': False}),
Points([1, 2, 3, 4, 2, 3],
group=2,
id=2,
attributes={'is_crowd': False}),
Bbox(1,
2,
2,
2,
group=2,
id=2,
attributes={'is_crowd': False}),
Points([1, 2, 0, 2, 4, 1],
label=5,
group=3,
id=3,
attributes={'is_crowd': False}),
Bbox(0,
1,
4,
1,
label=5,
group=3,
id=3,
attributes={'is_crowd': False}),
Points([0, 0, 1, 2, 3, 4], [0, 1, 2],
group=5,
id=5,
attributes={'is_crowd': False}),
Bbox(1,
2,
2,
2,
group=5,
id=5,
attributes={'is_crowd': False}),
],
attributes={'id': 1}),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable(str(i) for i in range(10)),
AnnotationType.points:
PointsCategories.from_iterable(
(i, None, [[0, 1], [1, 2]]) for i in range(10)),
})
with TestDir() as test_dir:
self._test_save_and_load(source_dataset,
CocoPersonKeypointsConverter.convert,
test_dir,
target_dataset=target_dataset)
def test_can_save_bboxes(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(id=1,
subset='train',
image=np.ones((16, 16, 3)),
annotations=[
Bbox(0,
4,
4,
8,
label=2,
attributes={
'occluded': True,
}),
Bbox(0,
4,
4,
4,
label=3,
attributes={
'visibility': 0.4,
}),
Bbox(2, 4, 4, 4, attributes={'ignored': True}),
]),
DatasetItem(id=2,
subset='val',
image=np.ones((8, 8, 3)),
annotations=[
Bbox(1, 2, 4, 2, label=3),
]),
DatasetItem(
id=3,
subset='test',
image=np.ones((5, 4, 3)) * 3,
),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(label)
for label in range(10)),
})
target_dataset = Dataset.from_iterable(
[
DatasetItem(id=1,
image=np.ones((16, 16, 3)),
annotations=[
Bbox(0,
4,
4,
8,
label=2,
attributes={
'occluded': True,
'visibility': 0.0,
'ignored': False,
}),
Bbox(0,
4,
4,
4,
label=3,
attributes={
'occluded': False,
'visibility': 0.4,
'ignored': False,
}),
Bbox(2,
4,
4,
4,
attributes={
'occluded': False,
'visibility': 1.0,
'ignored': True,
}),
]),
DatasetItem(id=2,
image=np.ones((8, 8, 3)),
annotations=[
Bbox(1,
2,
4,
2,
label=3,
attributes={
'occluded': False,
'visibility': 1.0,
'ignored': False,
}),
]),
DatasetItem(
id=3,
image=np.ones((5, 4, 3)) * 3,
),
],
categories={
AnnotationType.label:
LabelCategories.from_iterable('label_' + str(label)
for label in range(10)),
})
with TestDir() as test_dir:
self._test_save_and_load(source_dataset,
partial(MotSeqGtConverter.convert,
save_images=True),
test_dir,
target_dataset=target_dataset,
require_images=True)
def _load_categories(self, labels):
return { AnnotationType.label: LabelCategories().from_iterable(labels) }
def _parse_meta(context):
ev, el = next(context)
if not (ev == 'start' and el.tag == 'annotations'):
raise Exception("Unexpected token ")
categories = {}
frame_size = None
has_z_order = False
mode = 'annotation'
labels = OrderedDict()
label = None
# Recursive descent parser
el = None
states = ['annotations']
def accepted(expected_state, tag, next_state=None):
state = states[-1]
if state == expected_state and el is not None and el.tag == tag:
if not next_state:
next_state = tag
states.append(next_state)
return True
return False
def consumed(expected_state, tag):
state = states[-1]
if state == expected_state and el is not None and el.tag == tag:
states.pop()
return True
return False
for ev, el in context:
if ev == 'start':
if accepted('annotations', 'meta'): pass
elif accepted('meta', 'task'): pass
elif accepted('task', 'z_order'): pass
elif accepted('task', 'original_size'):
frame_size = [None, None]
elif accepted('original_size', 'height', next_state='frame_height'): pass
elif accepted('original_size', 'width', next_state='frame_width'): pass
elif accepted('task', 'labels'): pass
elif accepted('labels', 'label'):
label = { 'name': None, 'attributes': set() }
elif accepted('label', 'name', next_state='label_name'): pass
elif accepted('label', 'attributes'): pass
elif accepted('attributes', 'attribute'): pass
elif accepted('attribute', 'name', next_state='attr_name'): pass
elif accepted('annotations', 'image') or \
accepted('annotations', 'track') or \
accepted('annotations', 'tag'):
break
else:
pass
elif ev == 'end':
if consumed('meta', 'meta'):
break
elif consumed('task', 'task'): pass
elif consumed('z_order', 'z_order'):
has_z_order = (el.text == 'True')
elif consumed('original_size', 'original_size'): pass
elif consumed('frame_height', 'height'):
frame_size[0] = int(el.text)
elif consumed('frame_width', 'width'):
frame_size[1] = int(el.text)
elif consumed('label_name', 'name'):
label['name'] = el.text
elif consumed('attr_name', 'name'):
label['attributes'].add(el.text)
elif consumed('attribute', 'attribute'): pass
elif consumed('attributes', 'attributes'): pass
elif consumed('label', 'label'):
labels[label['name']] = label['attributes']
label = None
elif consumed('labels', 'labels'): pass
else:
pass
assert len(states) == 1 and states[0] == 'annotations', \
"Expected 'meta' section in the annotation file, path: %s" % states
common_attrs = ['occluded']
if has_z_order:
common_attrs.append('z_order')
if mode == 'interpolation':
common_attrs.append('keyframe')
common_attrs.append('outside')
label_cat = LabelCategories(attributes=common_attrs)
for label, attrs in labels.items():
label_cat.add(label, attributes=attrs)
categories[AnnotationType.label] = label_cat
return categories, frame_size