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[Datumaro] Fixes #1953

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merged 6 commits into from
Jul 29, 2020
Merged

[Datumaro] Fixes #1953

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2325385
Add absolute image path in rest api extractor
zhiltsov-max Jul 28, 2020
f64dbb5
Add default split for random split
zhiltsov-max Jul 28, 2020
0784d16
Fix image path in datumaro format
zhiltsov-max Jul 28, 2020
b10e148
Preserve bboxes in coco format
zhiltsov-max Jul 28, 2020
167e3cf
update changelog
zhiltsov-max Jul 28, 2020
7f84d43
Merge branch 'develop' into zm/dm-fixes
nmanovic Jul 29, 2020
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1 change: 1 addition & 0 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0

### Changed
- Smaller object details (<https://github.com/opencv/cvat/pull/1877>)
- `COCO` format does not convert bboxes to polygons on export (<https://github.com/opencv/cvat/pull/1953>)
- It is impossible to submit a DL model in OpenVINO format using UI. Now you can deploy new models on the server using serverless functions (<https://github.com/opencv/cvat/pull/1767>)
- Files and folders under share path are now alphabetically sorted

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2 changes: 1 addition & 1 deletion ...ps/dataset_manager/formats/datumaro/export_templates/plugins/cvat_rest_api_task_images.py
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Expand Up @@ -100,7 +100,7 @@ def __init__(self, url):
if entry.get('height') and entry.get('width'):
size = (entry['height'], entry['width'])
image = Image(data=self._make_image_loader(item_id),
path=item_filename, size=size)
path=self._image_local_path(item_id), size=size)
item = DatasetItem(id=item_id, image=image)
items.append((item.id, item))

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4 changes: 2 additions & 2 deletions datumaro/datumaro/plugins/coco_format/converter.py
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Expand Up @@ -298,8 +298,8 @@ def convert_instance(self, instance, item):
rles = mask_utils.merge(rles)
area = mask_utils.area(rles)
else:
x, y, w, h = bbox
segmentation = [[x, y, x + w, y, x + w, y + h, x, y + h]]
_, _, w, h = bbox
segmentation = []
area = w * h

elem = {
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3 changes: 2 additions & 1 deletion datumaro/datumaro/plugins/datumaro_format/converter.py
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Expand Up @@ -54,7 +54,8 @@ def write_item(self, item):
if item.has_image:
path = item.image.path
if self._context._save_images:
path = self._context._save_image(item)
path = self._context._make_image_filename(item)
self._context._save_image(item, path)

item_desc['image'] = {
'size': item.image.size,
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1 change: 1 addition & 0 deletions datumaro/datumaro/plugins/transforms.py
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Expand Up @@ -322,6 +322,7 @@ def build_cmdline_parser(cls, **kwargs):
parser = super().build_cmdline_parser(**kwargs)
parser.add_argument('-s', '--subset', action='append',
type=cls._split_arg, dest='splits',
default=[('train', 0.67), ('test', 0.33)],
help="Subsets in the form of: '<subset>:<ratio>' (repeatable)")
parser.add_argument('--seed', type=int, help="Random seed")
return parser
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32 changes: 15 additions & 17 deletions datumaro/tests/test_coco_format.py
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Expand Up @@ -4,8 +4,8 @@

from unittest import TestCase

from datumaro.components.project import (Project, Dataset)
from datumaro.components.extractor import (Extractor, DatasetItem,
from datumaro.components.project import Project, Dataset
from datumaro.components.extractor import (DatasetItem,
AnnotationType, Label, Mask, Points, Polygon, Bbox, Caption,
LabelCategories, PointsCategories
)
Expand All @@ -26,7 +26,6 @@

class CocoImporterTest(TestCase):
def test_can_import(self):

expected_dataset = Dataset.from_iterable([
DatasetItem(id='000000000001', image=np.ones((10, 5, 3)),
subset='val', attributes={'id': 1},
Expand Down Expand Up @@ -349,7 +348,6 @@ def test_can_save_and_load_labels(self):
CocoLabelsConverter.convert, test_dir)

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=[
Expand All @@ -373,11 +371,11 @@ def test_can_save_and_load_keypoints(self):
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)
),
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([
Expand All @@ -393,30 +391,30 @@ def test_can_save_and_load_keypoints(self):
Points([1, 2, 3, 4, 2, 3],
group=2, id=2,
attributes={'is_crowd': False}),
Polygon([1, 2, 3, 2, 3, 4, 1, 4],
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}),
Polygon([0, 1, 4, 1, 4, 2, 0, 2],
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}),
Polygon([1, 2, 3, 2, 3, 4, 1, 4],
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)
),
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:
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102 changes: 52 additions & 50 deletions datumaro/tests/test_datumaro_format.py
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Expand Up @@ -32,56 +32,58 @@ def _test_save_and_load(self, source_dataset, converter, test_dir,
compare_datasets_strict(self,
expected=target_dataset, actual=parsed_dataset)

label_categories = LabelCategories()
for i in range(5):
label_categories.add('cat' + str(i))

mask_categories = MaskCategories(
generate_colormap(len(label_categories.items)))

points_categories = PointsCategories()
for index, _ in enumerate(label_categories.items):
points_categories.add(index, ['cat1', 'cat2'], joints=[[0, 1]])

test_dataset = Dataset.from_iterable([
DatasetItem(id=100, subset='train', image=np.ones((10, 6, 3)),
annotations=[
Caption('hello', id=1),
Caption('world', id=2, group=5),
Label(2, id=3, attributes={
'x': 1,
'y': '2',
}),
Bbox(1, 2, 3, 4, label=4, id=4, z_order=1, attributes={
'score': 1.0,
}),
Bbox(5, 6, 7, 8, id=5, group=5),
Points([1, 2, 2, 0, 1, 1], label=0, id=5, z_order=4),
Mask(label=3, id=5, z_order=2, image=np.ones((2, 3))),
]),
DatasetItem(id=21, subset='train',
annotations=[
Caption('test'),
Label(2),
Bbox(1, 2, 3, 4, 5, id=42, group=42)
]),

DatasetItem(id=2, subset='val',
annotations=[
PolyLine([1, 2, 3, 4, 5, 6, 7, 8], id=11, z_order=1),
Polygon([1, 2, 3, 4, 5, 6, 7, 8], id=12, z_order=4),
]),

DatasetItem(id=42, subset='test',
attributes={'a1': 5, 'a2': '42'}),

DatasetItem(id=42),
DatasetItem(id=43, image=Image(path='1/b/c.qq', size=(2, 4))),
], categories={
AnnotationType.label: label_categories,
AnnotationType.mask: mask_categories,
AnnotationType.points: points_categories,
})
@property
def test_dataset(self):
label_categories = LabelCategories()
for i in range(5):
label_categories.add('cat' + str(i))

mask_categories = MaskCategories(
generate_colormap(len(label_categories.items)))

points_categories = PointsCategories()
for index, _ in enumerate(label_categories.items):
points_categories.add(index, ['cat1', 'cat2'], joints=[[0, 1]])

return Dataset.from_iterable([
DatasetItem(id=100, subset='train', image=np.ones((10, 6, 3)),
annotations=[
Caption('hello', id=1),
Caption('world', id=2, group=5),
Label(2, id=3, attributes={
'x': 1,
'y': '2',
}),
Bbox(1, 2, 3, 4, label=4, id=4, z_order=1, attributes={
'score': 1.0,
}),
Bbox(5, 6, 7, 8, id=5, group=5),
Points([1, 2, 2, 0, 1, 1], label=0, id=5, z_order=4),
Mask(label=3, id=5, z_order=2, image=np.ones((2, 3))),
]),
DatasetItem(id=21, subset='train',
annotations=[
Caption('test'),
Label(2),
Bbox(1, 2, 3, 4, 5, id=42, group=42)
]),

DatasetItem(id=2, subset='val',
annotations=[
PolyLine([1, 2, 3, 4, 5, 6, 7, 8], id=11, z_order=1),
Polygon([1, 2, 3, 4, 5, 6, 7, 8], id=12, z_order=4),
]),

DatasetItem(id=42, subset='test',
attributes={'a1': 5, 'a2': '42'}),

DatasetItem(id=42),
DatasetItem(id=43, image=Image(path='1/b/c.qq', size=(2, 4))),
], categories={
AnnotationType.label: label_categories,
AnnotationType.mask: mask_categories,
AnnotationType.points: points_categories,
})

def test_can_save_and_load(self):
with TestDir() as test_dir:
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