Example #1
0
    def test_dataset_with_save_dataset_meta_file(self):
        source_dataset = Dataset.from_iterable(
            [
                DatasetItem(id='a/b/1',
                            image=np.ones((8, 8, 3)),
                            subset='train',
                            annotations=[
                                Bbox(0, 2, 4, 2, label=2),
                                Bbox(0,
                                     1,
                                     2,
                                     3,
                                     label=1,
                                     attributes={
                                         'blur': '2',
                                         'expression': '0',
                                         'illumination': '0',
                                         'occluded': '0',
                                         'pose': '2',
                                         'invalid': '0'
                                     }),
                            ]),
            ],
            categories=['face', 'label_0', 'label_1'])

        with TestDir() as test_dir:
            WiderFaceConverter.convert(source_dataset,
                                       test_dir,
                                       save_images=True,
                                       save_dataset_meta=True)
            parsed_dataset = Dataset.import_from(test_dir, 'wider_face')

            self.assertTrue(osp.isfile(osp.join(test_dir,
                                                'dataset_meta.json')))
            compare_datasets(self, source_dataset, parsed_dataset)
Example #2
0
    def test_can_save_dataset_with_cyrillic_and_spaces_in_filename(self):
        source_dataset = Dataset.from_iterable([
            DatasetItem(id='кириллица с пробелом',
                        image=np.ones((8, 8, 3)),
                        annotations=[
                            Bbox(0,
                                 1,
                                 2,
                                 3,
                                 label=0,
                                 attributes={
                                     'blur': '2',
                                     'expression': '0',
                                     'illumination': '0',
                                     'occluded': '0',
                                     'pose': '2',
                                     'invalid': '0'
                                 }),
                        ]),
        ],
                                               categories=['face'])

        with TestDir() as test_dir:
            WiderFaceConverter.convert(source_dataset,
                                       test_dir,
                                       save_images=True)
            parsed_dataset = Dataset.import_from(test_dir, 'wider_face')

            compare_datasets(self,
                             source_dataset,
                             parsed_dataset,
                             require_images=True)
Example #3
0
    def test_can_save_and_load_with_no_save_images(self):
        source_dataset = Dataset.from_iterable([
            DatasetItem(id='1',
                        subset='train',
                        image=np.ones((8, 8, 3)),
                        annotations=[
                            Bbox(0, 2, 4, 2, label=1),
                            Bbox(0,
                                 1,
                                 2,
                                 3,
                                 label=0,
                                 attributes={
                                     'blur': '2',
                                     'expression': '0',
                                     'illumination': '0',
                                     'occluded': '0',
                                     'pose': '2',
                                     'invalid': '0'
                                 }),
                            Label(1),
                        ])
        ],
                                               categories=['face', 'label_0'])

        with TestDir() as test_dir:
            WiderFaceConverter.convert(source_dataset,
                                       test_dir,
                                       save_images=False)
            parsed_dataset = Dataset.import_from(test_dir, 'wider_face')

            compare_datasets(self, source_dataset, parsed_dataset)
Example #4
0
    def test_can_save_dataset_with_no_subsets(self):
        source_dataset = Dataset.from_iterable([
            DatasetItem(id='a/b/1',
                        image=np.ones((8, 8, 3)),
                        annotations=[
                            Bbox(0, 2, 4, 2),
                            Bbox(0,
                                 1,
                                 2,
                                 3,
                                 attributes={
                                     'blur': 2,
                                     'expression': 0,
                                     'illumination': 0,
                                     'occluded': 0,
                                     'pose': 2,
                                     'invalid': 0
                                 }),
                        ]),
        ],
                                               categories=[])

        with TestDir() as test_dir:
            WiderFaceConverter.convert(source_dataset,
                                       test_dir,
                                       save_images=True)
            parsed_dataset = Dataset.import_from(test_dir, 'wider_face')

            compare_datasets(self, source_dataset, parsed_dataset)
Example #5
0
    def test_can_save_dataset_with_no_subsets(self):
        source_dataset = Dataset.from_iterable(
            [
                DatasetItem(id='a/b/1',
                            image=np.ones((8, 8, 3)),
                            annotations=[
                                Bbox(0, 2, 4, 2, label=2),
                                Bbox(0,
                                     1,
                                     2,
                                     3,
                                     label=1,
                                     attributes={
                                         'blur': '2',
                                         'expression': '0',
                                         'illumination': '0',
                                         'occluded': '0',
                                         'pose': '2',
                                         'invalid': '0'
                                     }),
                            ]),
            ],
            categories={
                AnnotationType.label:
                LabelCategories.from_iterable('label_' + str(i)
                                              for i in range(3)),
            })

        with TestDir() as test_dir:
            WiderFaceConverter.convert(source_dataset,
                                       test_dir,
                                       save_images=True)
            parsed_dataset = Dataset.import_from(test_dir, 'wider_face')

            compare_datasets(self, source_dataset, parsed_dataset)
Example #6
0
    def test_can_save_dataset_with_non_widerface_attributes(self):
        source_dataset = Dataset.from_iterable([
            DatasetItem(id='a/b/1',
                        image=np.ones((8, 8, 3)),
                        annotations=[
                            Bbox(0, 2, 4, 2, label=0),
                            Bbox(0,
                                 1,
                                 2,
                                 3,
                                 label=0,
                                 attributes={
                                     'non-widerface attribute': '0',
                                     'blur': 1,
                                     'invalid': '1'
                                 }),
                            Bbox(1,
                                 1,
                                 2,
                                 2,
                                 label=0,
                                 attributes={'non-widerface attribute': '0'}),
                        ]),
        ],
                                               categories=['face'])

        target_dataset = Dataset.from_iterable([
            DatasetItem(id='a/b/1',
                        image=np.ones((8, 8, 3)),
                        annotations=[
                            Bbox(0, 2, 4, 2, label=0),
                            Bbox(0,
                                 1,
                                 2,
                                 3,
                                 label=0,
                                 attributes={
                                     'blur': '1',
                                     'invalid': '1'
                                 }),
                            Bbox(1, 1, 2, 2, label=0),
                        ]),
        ],
                                               categories=['face'])

        with TestDir() as test_dir:
            WiderFaceConverter.convert(source_dataset,
                                       test_dir,
                                       save_images=True)
            parsed_dataset = Dataset.import_from(test_dir, 'wider_face')

            compare_datasets(self, target_dataset, parsed_dataset)
Example #7
0
    def test_can_save_and_load_image_with_arbitrary_extension(self):
        dataset = Dataset.from_iterable([
            DatasetItem('q/1',
                        image=Image(path='q/1.JPEG', data=np.zeros(
                            (4, 3, 3)))),
            DatasetItem('a/b/c/2',
                        image=Image(path='a/b/c/2.bmp',
                                    data=np.zeros((3, 4, 3)))),
        ],
                                        categories=[])

        with TestDir() as test_dir:
            WiderFaceConverter.convert(dataset, test_dir, save_images=True)
            parsed_dataset = Dataset.import_from(test_dir, 'wider_face')

            compare_datasets(self,
                             dataset,
                             parsed_dataset,
                             require_images=True)
Example #8
0
    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),
                            Bbox(0,
                                 1,
                                 2,
                                 3,
                                 label=0,
                                 attributes={
                                     'blur': '2',
                                     'expression': '0',
                                     'illumination': '0',
                                     'occluded': '0',
                                     'pose': '2',
                                     'invalid': '0'
                                 }),
                            Label(1),
                        ]),
            DatasetItem(id='2',
                        subset='train',
                        image=np.ones((10, 10, 3)),
                        annotations=[
                            Bbox(0,
                                 2,
                                 4,
                                 2,
                                 label=0,
                                 attributes={
                                     'blur': '2',
                                     'expression': '0',
                                     'illumination': '1',
                                     'occluded': '0',
                                     'pose': '1',
                                     'invalid': '0'
                                 }),
                            Bbox(3,
                                 3,
                                 2,
                                 3,
                                 label=0,
                                 attributes={
                                     'blur': '0',
                                     'expression': '1',
                                     'illumination': '0',
                                     'occluded': '0',
                                     'pose': '2',
                                     'invalid': '0'
                                 }),
                            Bbox(2,
                                 1,
                                 2,
                                 3,
                                 label=0,
                                 attributes={
                                     'blur': '2',
                                     'expression': '0',
                                     'illumination': '0',
                                     'occluded': '0',
                                     'pose': '0',
                                     'invalid': '1'
                                 }),
                            Label(2),
                        ]),
            DatasetItem(
                id='3',
                subset='val',
                image=np.ones((8, 8, 3)),
                annotations=[
                    Bbox(
                        0,
                        1.1,
                        5.3,
                        2.1,
                        label=0,
                        attributes={
                            'blur':
                            '2',
                            'expression': '1',
                            'illumination': '0',
                            'occluded':
                            '0',
                            'pose':
                            '1',
                            'invalid': '0'
                        }),
                    Bbox(0, 2, 3, 2, label=0, attributes={'occluded': False}),
                    Bbox(0, 3, 4, 2, label=0, attributes={'occluded': True}),
                    Bbox(0, 2, 4, 2, label=0),
                    Bbox(0,
                         7,
                         3,
                         2,
                         label=0,
                         attributes={
                             'blur': '2',
                             'expression': '1',
                             'illumination': '0',
                             'occluded': '0',
                             'pose': '1',
                             'invalid': '0'
                         }),
                ]),
            DatasetItem(id='4', subset='val', image=np.ones((8, 8, 3))),
        ],
                                               categories=[
                                                   'face', 'label_0', 'label_1'
                                               ])

        with TestDir() as test_dir:
            WiderFaceConverter.convert(source_dataset,
                                       test_dir,
                                       save_images=True)
            parsed_dataset = Dataset.import_from(test_dir, 'wider_face')

            compare_datasets(self,
                             source_dataset,
                             parsed_dataset,
                             require_images=True)
Example #9
0
    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),
                                Bbox(0,
                                     1,
                                     2,
                                     3,
                                     attributes={
                                         'blur': 2,
                                         'expression': 0,
                                         'illumination': 0,
                                         'occluded': 0,
                                         'pose': 2,
                                         'invalid': 0
                                     }),
                                Label(0),
                            ]),
                DatasetItem(id='2',
                            subset='train',
                            image=np.ones((10, 10, 3)),
                            annotations=[
                                Bbox(0,
                                     2,
                                     4,
                                     2,
                                     attributes={
                                         'blur': 2,
                                         'expression': 0,
                                         'illumination': 1,
                                         'occluded': 0,
                                         'pose': 1,
                                         'invalid': 0
                                     }),
                                Bbox(3,
                                     3,
                                     2,
                                     3,
                                     attributes={
                                         'blur': 0,
                                         'expression': 1,
                                         'illumination': 0,
                                         'occluded': 0,
                                         'pose': 2,
                                         'invalid': 0
                                     }),
                                Bbox(2,
                                     1,
                                     2,
                                     3,
                                     attributes={
                                         'blur': 2,
                                         'expression': 0,
                                         'illumination': 0,
                                         'occluded': 0,
                                         'pose': 0,
                                         'invalid': 1
                                     }),
                                Label(1),
                            ]),
                DatasetItem(id='3',
                            subset='val',
                            image=np.ones((8, 8, 3)),
                            annotations=[
                                Bbox(0,
                                     1,
                                     5,
                                     2,
                                     attributes={
                                         'blur': 2,
                                         'expression': 1,
                                         'illumination': 0,
                                         'occluded': 0,
                                         'pose': 1,
                                         'invalid': 0
                                     }),
                                Bbox(0, 2, 3, 2),
                                Bbox(0, 2, 4, 2),
                                Bbox(0,
                                     7,
                                     3,
                                     2,
                                     attributes={
                                         'blur': 2,
                                         'expression': 1,
                                         'illumination': 0,
                                         'occluded': 0,
                                         'pose': 1,
                                         'invalid': 0
                                     }),
                            ]),
                DatasetItem(id='4', subset='val', image=np.ones((8, 8, 3))),
            ],
            categories={
                AnnotationType.label:
                LabelCategories.from_iterable('label_' + str(i)
                                              for i in range(3)),
            })

        with TestDir() as test_dir:
            WiderFaceConverter.convert(source_dataset,
                                       test_dir,
                                       save_images=True)
            parsed_dataset = Dataset.import_from(test_dir, 'wider_face')

            compare_datasets(self, source_dataset, parsed_dataset)