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660 lines
25 KiB
Python
660 lines
25 KiB
Python
import json
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import numpy as np
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import os
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import os.path as osp
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from unittest import TestCase
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from datumaro.components.project import Project
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from datumaro.components.extractor import (Extractor, DatasetItem,
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AnnotationType, Label, Mask, Points, Polygon, Bbox, Caption,
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LabelCategories, PointsCategories
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)
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from datumaro.plugins.coco_format.converter import (
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CocoConverter,
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CocoImageInfoConverter,
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CocoCaptionsConverter,
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CocoInstancesConverter,
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CocoPersonKeypointsConverter,
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CocoLabelsConverter,
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)
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from datumaro.plugins.coco_format.importer import CocoImporter
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from datumaro.util.image import save_image, Image
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from datumaro.util.test_utils import TestDir, compare_datasets
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class CocoImporterTest(TestCase):
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@staticmethod
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def generate_annotation():
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annotation = {
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'licenses': [],
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'info': {},
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'categories': [],
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'images': [],
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'annotations': [],
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}
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annotation['licenses'].append({
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'name': '',
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'id': 0,
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'url': '',
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})
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annotation['info'] = {
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'contributor': '',
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'date_created': '',
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'description': '',
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'url': '',
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'version': '',
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'year': '',
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}
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annotation['licenses'].append({
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'name': '',
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'id': 0,
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'url': '',
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})
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annotation['categories'].append({
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'id': 1,
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'name': 'TEST',
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'supercategory': '',
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})
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annotation['images'].append({
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"id": 1,
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"width": 5,
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"height": 10,
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"file_name": '000000000001.jpg',
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"license": 0,
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"flickr_url": '',
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"coco_url": '',
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"date_captured": 0,
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})
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annotation['annotations'].append({
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"id": 1,
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"image_id": 1,
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"category_id": 1,
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"segmentation": [[0, 0, 1, 0, 1, 2, 0, 2]],
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"area": 2,
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"bbox": [0, 0, 1, 2],
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"iscrowd": 0,
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})
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annotation['annotations'].append({
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"id": 2,
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"image_id": 1,
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"category_id": 1,
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"segmentation": {
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"counts": [
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0, 10,
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5, 5,
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5, 5,
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0, 10,
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10, 0],
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"size": [10, 5]},
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"area": 30,
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"bbox": [0, 0, 10, 4],
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"iscrowd": 1,
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})
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return annotation
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def COCO_dataset_generate(self, path):
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img_dir = osp.join(path, 'images', 'val')
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ann_dir = osp.join(path, 'annotations')
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os.makedirs(img_dir)
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os.makedirs(ann_dir)
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image = np.ones((10, 5, 3))
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save_image(osp.join(img_dir, '000000000001.jpg'), image)
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annotation = self.generate_annotation()
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with open(osp.join(ann_dir, 'instances_val.json'), 'w') as outfile:
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json.dump(annotation, outfile)
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def test_can_import(self):
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class DstExtractor(Extractor):
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def __iter__(self):
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return iter([
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DatasetItem(id=1, image=np.ones((10, 5, 3)), subset='val',
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annotations=[
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Polygon([0, 0, 1, 0, 1, 2, 0, 2], label=0,
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id=1, group=1, attributes={'is_crowd': False}),
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Mask(np.array(
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[[1, 0, 0, 1, 0]] * 5 +
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[[1, 1, 1, 1, 0]] * 5
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), label=0,
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id=2, group=2, attributes={'is_crowd': True}),
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]
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),
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])
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def categories(self):
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label_cat = LabelCategories()
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label_cat.add('TEST')
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return { AnnotationType.label: label_cat }
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with TestDir() as test_dir:
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self.COCO_dataset_generate(test_dir)
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dataset = Project.import_from(test_dir, 'coco').make_dataset()
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compare_datasets(self, DstExtractor(), dataset)
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def test_can_detect(self):
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with TestDir() as test_dir:
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self.COCO_dataset_generate(test_dir)
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self.assertTrue(CocoImporter.detect(test_dir))
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class CocoConverterTest(TestCase):
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def _test_save_and_load(self, source_dataset, converter, test_dir,
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target_dataset=None, importer_args=None):
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converter(source_dataset, test_dir)
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if importer_args is None:
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importer_args = {}
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parsed_dataset = CocoImporter()(test_dir, **importer_args).make_dataset()
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if target_dataset is None:
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target_dataset = source_dataset
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compare_datasets(self, expected=target_dataset, actual=parsed_dataset)
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def test_can_save_and_load_captions(self):
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class TestExtractor(Extractor):
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def __iter__(self):
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return iter([
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DatasetItem(id=1, subset='train',
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annotations=[
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Caption('hello', id=1, group=1),
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Caption('world', id=2, group=2),
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]),
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DatasetItem(id=2, subset='train',
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annotations=[
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Caption('test', id=3, group=3),
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]),
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DatasetItem(id=3, subset='val',
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annotations=[
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Caption('word', id=1, group=1),
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]
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),
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])
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with TestDir() as test_dir:
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self._test_save_and_load(TestExtractor(),
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CocoCaptionsConverter(), test_dir)
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def test_can_save_and_load_instances(self):
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label_categories = LabelCategories()
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for i in range(10):
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label_categories.add(str(i))
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categories = { AnnotationType.label: label_categories }
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class TestExtractor(Extractor):
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def __iter__(self):
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return iter([
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DatasetItem(id=1, subset='train', image=np.ones((4, 4, 3)),
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annotations=[
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# Bbox + single polygon
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Bbox(0, 1, 2, 2,
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label=2, group=1, id=1,
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attributes={ 'is_crowd': False }),
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Polygon([0, 1, 2, 1, 2, 3, 0, 3],
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attributes={ 'is_crowd': False },
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label=2, group=1, id=1),
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]),
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DatasetItem(id=2, subset='train', image=np.ones((4, 4, 3)),
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annotations=[
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# Mask + bbox
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Mask(np.array([
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[0, 1, 0, 0],
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[0, 1, 0, 0],
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[0, 1, 1, 1],
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[0, 0, 0, 0]],
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),
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attributes={ 'is_crowd': True },
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label=4, group=3, id=3),
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Bbox(1, 0, 2, 2, label=4, group=3, id=3,
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attributes={ 'is_crowd': True }),
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]),
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DatasetItem(id=3, subset='val', image=np.ones((4, 4, 3)),
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annotations=[
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# Bbox + mask
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Bbox(0, 1, 2, 2, label=4, group=3, id=3,
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attributes={ 'is_crowd': True }),
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Mask(np.array([
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[0, 0, 0, 0],
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[1, 1, 1, 0],
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[1, 1, 0, 0],
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[0, 0, 0, 0]],
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),
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attributes={ 'is_crowd': True },
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label=4, group=3, id=3),
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]),
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])
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def categories(self):
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return categories
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class DstExtractor(Extractor):
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def __iter__(self):
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return iter([
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DatasetItem(id=1, subset='train', image=np.ones((4, 4, 3)),
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annotations=[
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Polygon([0, 1, 2, 1, 2, 3, 0, 3],
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attributes={ 'is_crowd': False },
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label=2, group=1, id=1),
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]),
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DatasetItem(id=2, subset='train', image=np.ones((4, 4, 3)),
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annotations=[
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Mask(np.array([
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[0, 1, 0, 0],
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[0, 1, 0, 0],
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[0, 1, 1, 1],
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[0, 0, 0, 0]],
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),
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attributes={ 'is_crowd': True },
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label=4, group=3, id=3),
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]),
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DatasetItem(id=3, subset='val', image=np.ones((4, 4, 3)),
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annotations=[
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Mask(np.array([
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[0, 0, 0, 0],
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[1, 1, 1, 0],
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[1, 1, 0, 0],
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[0, 0, 0, 0]],
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),
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attributes={ 'is_crowd': True },
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label=4, group=3, id=3),
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]),
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])
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def categories(self):
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return categories
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with TestDir() as test_dir:
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self._test_save_and_load(TestExtractor(),
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CocoInstancesConverter(), test_dir,
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target_dataset=DstExtractor())
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def test_can_merge_polygons_on_loading(self):
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label_categories = LabelCategories()
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for i in range(10):
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label_categories.add(str(i))
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categories = { AnnotationType.label: label_categories }
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class TestExtractor(Extractor):
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def __iter__(self):
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return iter([
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DatasetItem(id=1, image=np.zeros((6, 10, 3)),
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annotations=[
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Polygon([0, 0, 4, 0, 4, 4],
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label=3, id=4, group=4),
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Polygon([5, 0, 9, 0, 5, 5],
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label=3, id=4, group=4),
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]
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),
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])
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def categories(self):
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return categories
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class TargetExtractor(TestExtractor):
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def __iter__(self):
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items = list(super().__iter__())
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items[0]._annotations = [
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Mask(np.array([
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[0, 1, 1, 1, 0, 1, 1, 1, 1, 0],
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[0, 0, 1, 1, 0, 1, 1, 1, 0, 0],
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[0, 0, 0, 1, 0, 1, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
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# only internal fragment (without the border),
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# but not everywhere...
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),
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label=3, id=4, group=4,
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attributes={ 'is_crowd': False }),
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]
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return iter(items)
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with TestDir() as test_dir:
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self._test_save_and_load(TestExtractor(),
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CocoInstancesConverter(), test_dir,
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importer_args={'merge_instance_polygons': True},
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target_dataset=TargetExtractor())
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def test_can_crop_covered_segments(self):
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label_categories = LabelCategories()
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for i in range(10):
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label_categories.add(str(i))
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class SrcTestExtractor(Extractor):
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def __iter__(self):
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return iter([
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DatasetItem(id=1, image=np.zeros((5, 5, 3)),
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annotations=[
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Mask(np.array([
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[0, 0, 1, 1, 1],
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[0, 0, 1, 1, 1],
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[1, 1, 0, 1, 1],
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[1, 1, 1, 0, 0],
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[1, 1, 1, 0, 0]],
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),
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label=2, id=1, z_order=0),
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Polygon([1, 1, 4, 1, 4, 4, 1, 4],
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label=1, id=2, z_order=1),
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]
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),
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])
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def categories(self):
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return { AnnotationType.label: label_categories }
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class DstTestExtractor(Extractor):
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def __iter__(self):
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return iter([
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DatasetItem(id=1, image=np.zeros((5, 5, 3)),
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annotations=[
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Mask(np.array([
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[0, 0, 1, 1, 1],
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[0, 0, 0, 0, 1],
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[1, 0, 0, 0, 1],
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[1, 0, 0, 0, 0],
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[1, 1, 1, 0, 0]],
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),
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attributes={ 'is_crowd': True },
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label=2, id=1, group=1),
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Polygon([1, 1, 4, 1, 4, 4, 1, 4],
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label=1, id=2, group=2,
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attributes={ 'is_crowd': False }),
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]
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),
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])
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def categories(self):
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return { AnnotationType.label: label_categories }
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with TestDir() as test_dir:
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self._test_save_and_load(SrcTestExtractor(),
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CocoInstancesConverter(crop_covered=True), test_dir,
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target_dataset=DstTestExtractor())
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def test_can_convert_polygons_to_mask(self):
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label_categories = LabelCategories()
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for i in range(10):
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label_categories.add(str(i))
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class SrcTestExtractor(Extractor):
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def __iter__(self):
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return iter([
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DatasetItem(id=1, image=np.zeros((6, 10, 3)),
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annotations=[
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Polygon([0, 0, 4, 0, 4, 4],
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label=3, id=4, group=4),
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Polygon([5, 0, 9, 0, 5, 5],
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label=3, id=4, group=4),
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]
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),
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])
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def categories(self):
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return { AnnotationType.label: label_categories }
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|
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class DstTestExtractor(Extractor):
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def __iter__(self):
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return iter([
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DatasetItem(id=1, image=np.zeros((6, 10, 3)),
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annotations=[
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Mask(np.array([
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[0, 1, 1, 1, 0, 1, 1, 1, 1, 0],
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[0, 0, 1, 1, 0, 1, 1, 1, 0, 0],
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[0, 0, 0, 1, 0, 1, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
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# only internal fragment (without the border),
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# but not everywhere...
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),
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attributes={ 'is_crowd': True },
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label=3, id=4, group=4),
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]
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),
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])
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def categories(self):
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return { AnnotationType.label: label_categories }
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with TestDir() as test_dir:
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self._test_save_and_load(SrcTestExtractor(),
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CocoInstancesConverter(segmentation_mode='mask'), test_dir,
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target_dataset=DstTestExtractor())
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|
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def test_can_convert_masks_to_polygons(self):
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label_categories = LabelCategories()
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for i in range(10):
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label_categories.add(str(i))
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|
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class SrcTestExtractor(Extractor):
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def __iter__(self):
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return iter([
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DatasetItem(id=1, image=np.zeros((5, 10, 3)),
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annotations=[
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Mask(np.array([
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[0, 1, 1, 1, 0, 1, 1, 1, 1, 0],
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[0, 0, 1, 1, 0, 1, 1, 1, 0, 0],
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[0, 0, 0, 1, 0, 1, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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]),
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label=3, id=4, group=4),
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]
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),
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])
|
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|
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def categories(self):
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return { AnnotationType.label: label_categories }
|
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|
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class DstTestExtractor(Extractor):
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def __iter__(self):
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return iter([
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DatasetItem(id=1, image=np.zeros((5, 10, 3)),
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annotations=[
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Polygon(
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[3.0, 2.5, 1.0, 0.0, 3.5, 0.0, 3.0, 2.5],
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label=3, id=4, group=4,
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attributes={ 'is_crowd': False }),
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Polygon(
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[5.0, 3.5, 4.5, 0.0, 8.0, 0.0, 5.0, 3.5],
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label=3, id=4, group=4,
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attributes={ 'is_crowd': False }),
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]
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),
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])
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def categories(self):
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return { AnnotationType.label: label_categories }
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with TestDir() as test_dir:
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self._test_save_and_load(SrcTestExtractor(),
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CocoInstancesConverter(segmentation_mode='polygons'), test_dir,
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target_dataset=DstTestExtractor())
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def test_can_save_and_load_images(self):
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class TestExtractor(Extractor):
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def __iter__(self):
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return iter([
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DatasetItem(id=1, subset='train'),
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DatasetItem(id=2, subset='train'),
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DatasetItem(id=2, subset='val'),
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DatasetItem(id=3, subset='val'),
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DatasetItem(id=4, subset='val'),
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DatasetItem(id=5, subset='test'),
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])
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with TestDir() as test_dir:
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self._test_save_and_load(TestExtractor(),
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CocoImageInfoConverter(), test_dir)
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|
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def test_can_save_and_load_labels(self):
|
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class TestExtractor(Extractor):
|
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def __iter__(self):
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return iter([
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DatasetItem(id=1, subset='train',
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annotations=[
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Label(4, id=1, group=1),
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Label(9, id=2, group=2),
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]),
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DatasetItem(id=2, subset='train',
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annotations=[
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Label(4, id=4, group=4),
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]),
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DatasetItem(id=3, subset='val',
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annotations=[
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Label(2, id=1, group=1),
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]),
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])
|
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|
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def categories(self):
|
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label_categories = LabelCategories()
|
|
for i in range(10):
|
|
label_categories.add(str(i))
|
|
return {
|
|
AnnotationType.label: label_categories,
|
|
}
|
|
|
|
with TestDir() as test_dir:
|
|
self._test_save_and_load(TestExtractor(),
|
|
CocoLabelsConverter(), test_dir)
|
|
|
|
def test_can_save_and_load_keypoints(self):
|
|
label_categories = LabelCategories()
|
|
points_categories = PointsCategories()
|
|
for i in range(10):
|
|
label_categories.add(str(i))
|
|
points_categories.add(i, [])
|
|
categories = {
|
|
AnnotationType.label: label_categories,
|
|
AnnotationType.points: points_categories,
|
|
}
|
|
|
|
class TestExtractor(Extractor):
|
|
def __iter__(self):
|
|
return iter([
|
|
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),
|
|
]),
|
|
DatasetItem(id=2, subset='train',
|
|
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),
|
|
]),
|
|
])
|
|
|
|
def categories(self):
|
|
return categories
|
|
|
|
class DstTestExtractor(TestExtractor):
|
|
def __iter__(self):
|
|
return iter([
|
|
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}),
|
|
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,
|
|
attributes={'is_crowd': False}),
|
|
Polygon([1, 2, 3, 2, 3, 4, 1, 4],
|
|
group=3, id=3,
|
|
attributes={'is_crowd': False}),
|
|
]),
|
|
])
|
|
|
|
with TestDir() as test_dir:
|
|
self._test_save_and_load(TestExtractor(),
|
|
CocoPersonKeypointsConverter(), test_dir,
|
|
target_dataset=DstTestExtractor())
|
|
|
|
def test_can_save_dataset_with_no_subsets(self):
|
|
class TestExtractor(Extractor):
|
|
def __iter__(self):
|
|
return iter([
|
|
DatasetItem(id=1, annotations=[
|
|
Label(2, id=1, group=1),
|
|
]),
|
|
|
|
DatasetItem(id=2, annotations=[
|
|
Label(3, id=2, group=2),
|
|
]),
|
|
])
|
|
|
|
def categories(self):
|
|
label_cat = LabelCategories()
|
|
point_cat = PointsCategories()
|
|
for label in range(10):
|
|
label_cat.add('label_' + str(label))
|
|
point_cat.add(label)
|
|
return {
|
|
AnnotationType.label: label_cat,
|
|
AnnotationType.points: point_cat,
|
|
}
|
|
|
|
with TestDir() as test_dir:
|
|
self._test_save_and_load(TestExtractor(),
|
|
CocoConverter(), test_dir)
|
|
|
|
def test_can_save_dataset_with_image_info(self):
|
|
class TestExtractor(Extractor):
|
|
def __iter__(self):
|
|
return iter([
|
|
DatasetItem(id=1, image=Image(path='1.jpg', size=(10, 15))),
|
|
])
|
|
|
|
with TestDir() as test_dir:
|
|
self._test_save_and_load(TestExtractor(),
|
|
CocoConverter(tasks='image_info'), test_dir) |