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[Datumaro] Update COCO tests #1669

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Jun 10, 2020
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[Datumaro] Update COCO tests #1669

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189 changes: 44 additions & 145 deletions datumaro/tests/test_coco_format.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,4 @@
import json
import numpy as np
import os
import os.path as osp

from unittest import TestCase
Expand All @@ -19,94 +17,13 @@
CocoLabelsConverter,
)
from datumaro.plugins.coco_format.importer import CocoImporter
from datumaro.util.image import save_image, Image
from datumaro.util.image import Image
from datumaro.util.test_utils import TestDir, compare_datasets


class CocoImporterTest(TestCase):
@staticmethod
def generate_annotation():
annotation = {
'licenses': [],
'info': {},
'categories': [],
'images': [],
'annotations': [],
}
annotation['licenses'].append({
'name': '',
'id': 0,
'url': '',
})
annotation['info'] = {
'contributor': '',
'date_created': '',
'description': '',
'url': '',
'version': '',
'year': '',
}
annotation['licenses'].append({
'name': '',
'id': 0,
'url': '',
})
annotation['categories'].append({
'id': 1,
'name': 'TEST',
'supercategory': '',
})
annotation['images'].append({
"id": 1,
"width": 5,
"height": 10,
"file_name": '000000000001.jpg',
"license": 0,
"flickr_url": '',
"coco_url": '',
"date_captured": 0,
})
annotation['annotations'].append({
"id": 1,
"image_id": 1,
"category_id": 1,
"segmentation": [[0, 0, 1, 0, 1, 2, 0, 2]],
"area": 2,
"bbox": [0, 0, 1, 2],
"iscrowd": 0,
})
annotation['annotations'].append({
"id": 2,
"image_id": 1,
"category_id": 1,
"segmentation": {
"counts": [
0, 10,
5, 5,
5, 5,
0, 10,
10, 0],
"size": [10, 5]},
"area": 30,
"bbox": [0, 0, 10, 4],
"iscrowd": 1,
})
return annotation

def COCO_dataset_generate(self, path):
img_dir = osp.join(path, 'images', 'val')
ann_dir = osp.join(path, 'annotations')
os.makedirs(img_dir)
os.makedirs(ann_dir)

image = np.ones((10, 5, 3))
save_image(osp.join(img_dir, '000000000001.jpg'), image)

annotation = self.generate_annotation()

with open(osp.join(ann_dir, 'instances_val.json'), 'w') as outfile:
json.dump(annotation, outfile)
DUMMY_DATASET_DIR = osp.join(osp.dirname(__file__), 'assets', 'coco_dataset')

class CocoImporterTest(TestCase):
def test_can_import(self):
class DstExtractor(Extractor):
def __iter__(self):
Expand All @@ -129,18 +46,13 @@ def categories(self):
label_cat.add('TEST')
return { AnnotationType.label: label_cat }

with TestDir() as test_dir:
self.COCO_dataset_generate(test_dir)

dataset = Project.import_from(test_dir, 'coco').make_dataset()
dataset = Project.import_from(DUMMY_DATASET_DIR, 'coco') \
.make_dataset()

compare_datasets(self, DstExtractor(), dataset)
compare_datasets(self, DstExtractor(), dataset)

def test_can_detect(self):
with TestDir() as test_dir:
self.COCO_dataset_generate(test_dir)

self.assertTrue(CocoImporter.detect(test_dir))
self.assertTrue(CocoImporter.detect(DUMMY_DATASET_DIR))

class CocoConverterTest(TestCase):
def _test_save_and_load(self, source_dataset, converter, test_dir,
Expand Down Expand Up @@ -282,7 +194,7 @@ def test_can_merge_polygons_on_loading(self):
label_categories.add(str(i))
categories = { AnnotationType.label: label_categories }

class TestExtractor(Extractor):
class SrcExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(id=1, image=np.zeros((6, 10, 3)),
Expand All @@ -298,30 +210,35 @@ def __iter__(self):
def categories(self):
return categories

class TargetExtractor(TestExtractor):
class DstExtractor(Extractor):
def __iter__(self):
items = list(super().__iter__())
items[0]._annotations = [
Mask(np.array([
[0, 1, 1, 1, 0, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 1, 0, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
# only internal fragment (without the border),
# but not everywhere...
),
label=3, id=4, group=4,
attributes={ 'is_crowd': False }),
]
return iter(items)
return iter([
DatasetItem(id=1, image=np.zeros((6, 10, 3)),
annotations=[
Mask(np.array([
[0, 1, 1, 1, 0, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 1, 0, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
# only internal fragment (without the border),
# but not everywhere...
),
label=3, id=4, group=4,
attributes={ 'is_crowd': False }),
]
),
])

def categories(self):
return categories

with TestDir() as test_dir:
self._test_save_and_load(TestExtractor(),
self._test_save_and_load(SrcExtractor(),
CocoInstancesConverter(), test_dir,
importer_args={'merge_instance_polygons': True},
target_dataset=TargetExtractor())
target_dataset=DstExtractor())

def test_can_crop_covered_segments(self):
label_categories = LabelCategories()
Expand Down Expand Up @@ -435,7 +352,7 @@ def test_can_convert_masks_to_polygons(self):
for i in range(10):
label_categories.add(str(i))

class SrcTestExtractor(Extractor):
class SrcExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(id=1, image=np.zeros((5, 10, 3)),
Expand All @@ -455,7 +372,7 @@ def __iter__(self):
def categories(self):
return { AnnotationType.label: label_categories }

class DstTestExtractor(Extractor):
class DstExtractor(Extractor):
def __iter__(self):
return iter([
DatasetItem(id=1, image=np.zeros((5, 10, 3)),
Expand All @@ -476,9 +393,9 @@ def categories(self):
return { AnnotationType.label: label_categories }

with TestDir() as test_dir:
self._test_save_and_load(SrcTestExtractor(),
self._test_save_and_load(SrcExtractor(),
CocoInstancesConverter(segmentation_mode='polygons'), test_dir,
target_dataset=DstTestExtractor())
target_dataset=DstExtractor())

def test_can_save_and_load_images(self):
class TestExtractor(Extractor):
Expand Down Expand Up @@ -506,16 +423,8 @@ def __iter__(self):
annotations=[
Label(4, id=1, group=1),
Label(9, id=2, group=2),
]),
DatasetItem(id=2, subset='train',
annotations=[
Label(4, id=4, group=4),
]),

DatasetItem(id=3, subset='val',
annotations=[
Label(2, id=1, group=1),
]),
]
),
])

def categories(self):
Expand Down Expand Up @@ -555,21 +464,16 @@ def __iter__(self):
# 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),
]),
DatasetItem(id=2, subset='train', image=np.zeros((5, 4, 3)),
annotations=[

# 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),
]),

DatasetItem(id=3, subset='val',
annotations=[
# Solitary keypoints with no label
Points([0, 0, 1, 2, 3, 4], [0, 1, 2], id=3),
]),
Points([0, 0, 1, 2, 3, 4], [0, 1, 2], id=5),
])
])

def categories(self):
Expand All @@ -593,24 +497,19 @@ def __iter__(self):
Polygon([1, 2, 3, 2, 3, 4, 1, 4],
group=2, id=2,
attributes={'is_crowd': False}),
]),
DatasetItem(id=2, subset='train',
annotations=[

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],
label=5, group=3, id=3,
attributes={'is_crowd': False}),
]),

DatasetItem(id=3, subset='val',
annotations=[
Points([0, 0, 1, 2, 3, 4], [0, 1, 2],
group=3, id=3,
group=5, id=5,
attributes={'is_crowd': False}),
Polygon([1, 2, 3, 2, 3, 4, 1, 4],
group=3, id=3,
group=5, id=5,
attributes={'is_crowd': False}),
]),
])
Expand Down