/
test_mvtec_format.py
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/
test_mvtec_format.py
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from unittest import TestCase
import cv2
import numpy as np
from datumaro.components.annotation import AnnotationType, Bbox, Label, LabelCategories, Mask
from datumaro.components.dataset import Dataset
from datumaro.components.dataset_base import DatasetItem
from datumaro.components.environment import Environment
from datumaro.components.media import Image
from datumaro.plugins.data_formats.mvtec.exporter import MvtecExporter
from datumaro.plugins.data_formats.mvtec.importer import (
MvtecClassificationImporter,
MvtecDetectionImporter,
MvtecSegmentationImporter,
)
from tests.requirements import Requirements, mark_requirement
from tests.utils.assets import get_test_asset_path
from tests.utils.test_utils import TestDir, compare_datasets
class MVTecFormatTest(TestCase):
@mark_requirement(Requirements.DATUM_GENERAL_REQ)
def test_can_save_and_load_classification(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(
id="label_0/000",
media=Image.from_numpy(data=np.ones((8, 8, 3))),
annotations=[Label(0)],
),
DatasetItem(
id="label_1/000",
media=Image.from_numpy(data=np.ones((8, 8, 3))),
annotations=[Label(1)],
),
],
categories={
AnnotationType.label: LabelCategories.from_iterable(
"label_" + str(label) for label in range(2)
),
},
)
with TestDir() as test_dir:
MvtecExporter.convert(source_dataset, test_dir, save_media=True)
parsed_dataset = Dataset.import_from(test_dir, "mvtec_classification")
compare_datasets(self, source_dataset, parsed_dataset, require_media=True)
@mark_requirement(Requirements.DATUM_GENERAL_REQ)
def test_can_save_and_load_segmentation(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(
id="label_0/000",
media=Image.from_numpy(data=np.ones((8, 8, 3))),
annotations=[Label(0)],
),
DatasetItem(
id="label_1/000",
media=Image.from_numpy(data=np.ones((8, 8, 3))),
annotations=[Mask(image=np.ones((8, 8), dtype=np.uint8), label=1)],
),
DatasetItem(
id="label_2/000",
media=Image.from_numpy(data=np.ones((8, 8, 3))),
annotations=[Mask(image=np.zeros((8, 8), dtype=np.uint8), label=2)],
),
],
categories={
AnnotationType.label: LabelCategories.from_iterable(
"label_" + str(label) for label in range(3)
),
},
)
with TestDir() as test_dir:
MvtecExporter.convert(source_dataset, test_dir, save_media=True)
parsed_dataset = Dataset.import_from(test_dir, "mvtec_segmentation")
compare_datasets(self, source_dataset, parsed_dataset, require_media=True)
@mark_requirement(Requirements.DATUM_GENERAL_REQ)
def test_can_save_and_load_detection(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(
id="label_0/000",
media=Image.from_numpy(data=np.ones((8, 8, 3))),
annotations=[Label(0)],
),
DatasetItem(
id="label_1/000",
media=Image.from_numpy(data=np.ones((8, 8, 3))),
annotations=[Bbox(x=0, y=0, w=3, h=3, label=1)],
),
DatasetItem(
id="label_2/000",
media=Image.from_numpy(data=np.ones((8, 8, 3))),
annotations=[Bbox(x=4, y=4, w=3, h=3, label=2)],
),
],
categories={
AnnotationType.label: LabelCategories.from_iterable(
"label_" + str(label) for label in range(3)
),
},
)
with TestDir() as test_dir:
MvtecExporter.convert(source_dataset, test_dir, save_media=True)
parsed_dataset = Dataset.import_from(test_dir, "mvtec_detection")
compare_datasets(self, source_dataset, parsed_dataset, require_media=True)
DUMMY_DATASET_DIR = get_test_asset_path("mvtec_dataset", "category_0")
class MvtecImporterTest(TestCase):
@mark_requirement(Requirements.DATUM_GENERAL_REQ)
def test_can_import_classification(self):
expected_dataset = Dataset.from_iterable(
[
DatasetItem(
id="label_0/000",
media=Image.from_numpy(data=np.ones((8, 8, 3))),
annotations=[Label(label=0)],
),
DatasetItem(
id="label_1/000",
media=Image.from_numpy(data=np.ones((8, 8, 3))),
annotations=[Label(label=1)],
),
DatasetItem(
id="label_2/000",
media=Image.from_numpy(data=np.ones((8, 8, 3))),
annotations=[Label(label=2)],
),
],
categories={
AnnotationType.label: LabelCategories.from_iterable(
"label_" + str(label) for label in range(3)
),
},
)
dataset = Dataset.import_from(DUMMY_DATASET_DIR, "mvtec_classification")
compare_datasets(self, expected_dataset, dataset, require_media=True)
@mark_requirement(Requirements.DATUM_GENERAL_REQ)
def test_can_import_segmentation(self):
mask_label_1 = np.pad(np.ones((4, 4), dtype=np.uint8), ((0, 4), (0, 4)), "constant")
mask_label_2 = np.pad(np.ones((4, 4), dtype=np.uint8), ((4, 0), (4, 0)), "constant")
expected_dataset = Dataset.from_iterable(
[
DatasetItem(
id="label_0/000",
media=Image.from_numpy(data=np.ones((8, 8, 3))),
annotations=[Label(label=0)],
),
DatasetItem(
id="label_1/000",
media=Image.from_numpy(data=np.ones((8, 8, 3))),
annotations=[
Mask(
image=mask_label_1,
label=1,
)
],
),
DatasetItem(
id="label_2/000",
media=Image.from_numpy(data=np.ones((8, 8, 3))),
annotations=[Mask(image=mask_label_2, label=2)],
),
],
categories={
AnnotationType.label: LabelCategories.from_iterable(
"label_" + str(label) for label in range(3)
),
},
)
dataset = Dataset.import_from(DUMMY_DATASET_DIR, "mvtec_segmentation")
compare_datasets(self, expected_dataset, dataset, require_media=True)
@mark_requirement(Requirements.DATUM_GENERAL_REQ)
def test_can_import_detection(self):
expected_dataset = Dataset.from_iterable(
[
DatasetItem(
id="label_0/000",
media=Image.from_numpy(data=np.ones((8, 8, 3))),
annotations=[Label(label=0)],
),
DatasetItem(
id="label_1/000",
media=Image.from_numpy(data=np.ones((8, 8, 3))),
annotations=[Bbox(x=0, y=0, w=3, h=3, label=1)],
),
DatasetItem(
id="label_2/000",
media=Image.from_numpy(data=np.ones((8, 8, 3))),
annotations=[Bbox(x=4, y=4, w=3, h=3, label=2)],
),
],
categories={
AnnotationType.label: LabelCategories.from_iterable(
"label_" + str(label) for label in range(3)
),
},
)
dataset = Dataset.import_from(DUMMY_DATASET_DIR, "mvtec_detection")
compare_datasets(self, expected_dataset, dataset, require_media=True)
@mark_requirement(Requirements.DATUM_GENERAL_REQ)
def test_can_detect_mvtec(self):
env = Environment()
path = DUMMY_DATASET_DIR
detected_formats = env.detect_dataset(path)
self.assertEqual(
sorted(detected_formats),
[
MvtecClassificationImporter.NAME,
MvtecDetectionImporter.NAME,
MvtecSegmentationImporter.NAME,
],
)
class MVTecExporterTest(TestCase):
@mark_requirement(Requirements.DATUM_GENERAL_REQ)
def test_segmentation_masks_saved_as_binary_image(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(
id="label_1/000",
media=Image.from_numpy(data=np.ones((8, 8, 3))),
annotations=[Mask(image=np.ones((8, 8), dtype=np.uint8), label=1)],
),
],
categories={
AnnotationType.label: LabelCategories.from_iterable(
"label_" + str(label) for label in range(2)
),
},
)
with TestDir() as test_dir:
MvtecExporter.convert(source_dataset, test_dir, save_media=True)
assert cv2.imread(test_dir + "/ground_truth/label_1/000_mask.png").max() == 255
@mark_requirement(Requirements.DATUM_GENERAL_REQ)
def test_detection_masks_saved_as_binary_image(self):
source_dataset = Dataset.from_iterable(
[
DatasetItem(
id="label_1/000",
media=Image.from_numpy(data=np.ones((8, 8, 3))),
annotations=[Bbox(0, 0, 8, 8, label=1)],
),
],
categories={
AnnotationType.label: LabelCategories.from_iterable(
"label_" + str(label) for label in range(2)
),
},
)
with TestDir() as test_dir:
MvtecExporter.convert(source_dataset, test_dir, save_media=True)
assert cv2.imread(test_dir + "/ground_truth/label_1/000_mask.png").max() == 255