MediaPipe Team
162 lines
6.5 KiB
Python
162 lines
6.5 KiB
Python
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.s
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import collections
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import os
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import shutil
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from typing import NamedTuple
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import unittest
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from absl import flags
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from absl.testing import parameterized
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import tensorflow as tf
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from mediapipe.model_maker.python.vision.gesture_recognizer import dataset
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from mediapipe.python.solutions import hands as mp_hands
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from mediapipe.tasks.python.test import test_utils
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FLAGS = flags.FLAGS
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_TEST_DATA_DIRNAME = 'raw_data'
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class DatasetTest(tf.test.TestCase, parameterized.TestCase):
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def test_split(self):
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input_data_dir = test_utils.get_test_data_path(_TEST_DATA_DIRNAME)
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data = dataset.Dataset.from_folder(
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dirname=input_data_dir, hparams=dataset.HandDataPreprocessingParams())
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train_data, test_data = data.split(0.5)
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self.assertLen(train_data, 17)
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for _, elem in enumerate(train_data.gen_tf_dataset(is_training=True)):
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self.assertEqual(elem[0].shape, (1, 128))
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self.assertEqual(elem[1].shape, ([1, 4]))
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self.assertEqual(train_data.num_classes, 4)
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self.assertEqual(train_data.label_names, ['none', 'call', 'four', 'rock'])
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self.assertLen(test_data, 18)
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for _, elem in enumerate(test_data.gen_tf_dataset(is_training=True)):
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self.assertEqual(elem[0].shape, (1, 128))
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self.assertEqual(elem[1].shape, ([1, 4]))
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self.assertEqual(test_data.num_classes, 4)
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self.assertEqual(test_data.label_names, ['none', 'call', 'four', 'rock'])
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def test_from_folder(self):
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input_data_dir = test_utils.get_test_data_path(_TEST_DATA_DIRNAME)
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data = dataset.Dataset.from_folder(
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dirname=input_data_dir, hparams=dataset.HandDataPreprocessingParams())
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for _, elem in enumerate(data.gen_tf_dataset(is_training=True)):
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self.assertEqual(elem[0].shape, (1, 128))
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self.assertEqual(elem[1].shape, ([1, 4]))
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self.assertLen(data, 35)
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self.assertEqual(data.num_classes, 4)
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self.assertEqual(data.label_names, ['none', 'call', 'four', 'rock'])
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def test_create_dataset_from_empty_folder_raise_value_error(self):
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with self.assertRaisesRegex(ValueError, 'Image dataset directory is empty'):
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dataset.Dataset.from_folder(
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dirname=self.get_temp_dir(),
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hparams=dataset.HandDataPreprocessingParams())
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def test_create_dataset_from_folder_without_none_raise_value_error(self):
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input_data_dir = test_utils.get_test_data_path(_TEST_DATA_DIRNAME)
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tmp_dir = self.create_tempdir()
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# Copy input dataset to a temporary directory and skip 'None' directory.
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for name in os.listdir(input_data_dir):
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if name == 'none':
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continue
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src_dir = os.path.join(input_data_dir, name)
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dst_dir = os.path.join(tmp_dir, name)
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shutil.copytree(src_dir, dst_dir)
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with self.assertRaisesRegex(ValueError,
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'Label set does not contain label "None"'):
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dataset.Dataset.from_folder(
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dirname=tmp_dir, hparams=dataset.HandDataPreprocessingParams())
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def test_create_dataset_from_folder_with_capital_letter_in_folder_name(self):
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input_data_dir = test_utils.get_test_data_path(_TEST_DATA_DIRNAME)
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tmp_dir = self.create_tempdir()
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# Copy input dataset to a temporary directory and change the base folder
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# name to upper case letter, e.g. 'none' -> 'NONE'
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for name in os.listdir(input_data_dir):
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src_dir = os.path.join(input_data_dir, name)
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dst_dir = os.path.join(tmp_dir, name.upper())
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shutil.copytree(src_dir, dst_dir)
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upper_base_folder_name = list(os.listdir(tmp_dir))
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self.assertCountEqual(upper_base_folder_name,
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['CALL', 'FOUR', 'NONE', 'ROCK'])
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data = dataset.Dataset.from_folder(
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dirname=tmp_dir, hparams=dataset.HandDataPreprocessingParams())
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for _, elem in enumerate(data.gen_tf_dataset(is_training=True)):
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self.assertEqual(elem[0].shape, (1, 128))
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self.assertEqual(elem[1].shape, ([1, 4]))
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self.assertLen(data, 35)
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self.assertEqual(data.num_classes, 4)
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self.assertEqual(data.label_names, ['NONE', 'CALL', 'FOUR', 'ROCK'])
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@parameterized.named_parameters(
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dict(
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testcase_name='invalid_field_name_multi_hand_landmark',
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hand=collections.namedtuple('Hand', [
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'multi_hand_landmark', 'multi_hand_world_landmarks',
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'multi_handedness'
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])(1, 2, 3)),
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dict(
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testcase_name='invalid_field_name_multi_hand_world_landmarks',
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hand=collections.namedtuple('Hand', [
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'multi_hand_landmarks', 'multi_hand_world_landmark',
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'multi_handedness'
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])(1, 2, 3)),
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dict(
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testcase_name='invalid_field_name_multi_handed',
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hand=collections.namedtuple('Hand', [
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'multi_hand_landmarks', 'multi_hand_world_landmarks',
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'multi_handed'
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])(1, 2, 3)),
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dict(
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testcase_name='multi_hand_landmarks_is_none',
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hand=collections.namedtuple('Hand', [
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'multi_hand_landmarks', 'multi_hand_world_landmarks',
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'multi_handedness'
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])(None, 2, 3)),
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dict(
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testcase_name='multi_hand_world_landmarks_is_none',
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hand=collections.namedtuple('Hand', [
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'multi_hand_landmarks', 'multi_hand_world_landmarks',
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'multi_handedness'
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])(1, None, 3)),
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dict(
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testcase_name='multi_handedness_is_none',
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hand=collections.namedtuple('Hand', [
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'multi_hand_landmarks', 'multi_hand_world_landmarks',
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'multi_handedness'
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])(1, 2, None)),
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)
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def test_create_dataset_from_invalid_hand_data(self, hand: NamedTuple):
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with unittest.mock.patch.object(
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mp_hands.Hands, 'process', return_value=hand):
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input_data_dir = test_utils.get_test_data_path(_TEST_DATA_DIRNAME)
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with self.assertRaisesRegex(ValueError, 'No valid hand is detected'):
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dataset.Dataset.from_folder(
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dirname=input_data_dir,
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hparams=dataset.HandDataPreprocessingParams())
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if __name__ == '__main__':
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tf.test.main()
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