262 lines
12 KiB
Python
262 lines
12 KiB
Python
"""Copyright 2019 The MediaPipe Authors.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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Tests for media_sequence.py.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import tensorflow.compat.v1 as tf
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from mediapipe.util.sequence import media_sequence as ms
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class MediaSequenceTest(tf.test.TestCase):
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def test_expected_functions_are_defined(self):
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# The code from media_sequence_util is already tested, but this test ensures
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# that we actually generate the expected methods. We only test one per
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# feature and the only test is to not crash with undefined attributes. By
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# passing in a value, we also ensure that the types are correct because the
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# underlying code crashes with a type mismatch.
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example = tf.train.SequenceExample()
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# context
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ms.set_example_id(b"string", example)
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ms.set_example_dataset_name(b"string", example)
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ms.set_clip_media_id(b"string", example)
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ms.set_clip_alternative_media_id(b"string", example)
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ms.set_clip_encoded_media_bytes(b"string", example)
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ms.set_clip_encoded_media_start_timestamp(47, example)
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ms.set_clip_data_path(b"string", example)
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ms.set_clip_start_timestamp(47, example)
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ms.set_clip_end_timestamp(47, example)
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ms.set_clip_label_string((b"string", b"test"), example)
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ms.set_clip_label_index((47, 49), example)
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ms.set_clip_label_confidence((0.47, 0.49), example)
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ms.set_segment_start_timestamp((47, 49), example)
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ms.set_segment_start_index((47, 49), example)
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ms.set_segment_end_timestamp((47, 49), example)
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ms.set_segment_end_index((47, 49), example)
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ms.set_segment_label_index((47, 49), example)
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ms.set_segment_label_string((b"test", b"strings"), example)
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ms.set_segment_label_confidence((0.47, 0.49), example)
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ms.set_image_format(b"test", example)
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ms.set_image_channels(47, example)
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ms.set_image_colorspace(b"test", example)
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ms.set_image_height(47, example)
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ms.set_image_width(47, example)
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ms.set_image_frame_rate(0.47, example)
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ms.set_image_data_path(b"test", example)
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ms.set_forward_flow_format(b"test", example)
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ms.set_forward_flow_channels(47, example)
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ms.set_forward_flow_colorspace(b"test", example)
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ms.set_forward_flow_height(47, example)
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ms.set_forward_flow_width(47, example)
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ms.set_forward_flow_frame_rate(0.47, example)
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ms.set_class_segmentation_format(b"test", example)
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ms.set_class_segmentation_height(47, example)
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ms.set_class_segmentation_width(47, example)
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ms.set_class_segmentation_class_label_string((b"test", b"strings"), example)
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ms.set_class_segmentation_class_label_index((47, 49), example)
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ms.set_instance_segmentation_format(b"test", example)
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ms.set_instance_segmentation_height(47, example)
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ms.set_instance_segmentation_width(47, example)
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ms.set_instance_segmentation_object_class_index((47, 49), example)
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ms.set_bbox_parts((b"HEAD", b"TOE"), example)
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# feature lists
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ms.add_image_encoded(b"test", example)
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ms.add_image_multi_encoded([b"test", b"test"], example)
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ms.add_image_timestamp(47, example)
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ms.add_forward_flow_encoded(b"test", example)
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ms.add_forward_flow_multi_encoded([b"test", b"test"], example)
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ms.add_forward_flow_timestamp(47, example)
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ms.add_bbox_ymin((0.47, 0.49), example)
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ms.add_bbox_xmin((0.47, 0.49), example)
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ms.add_bbox_ymax((0.47, 0.49), example)
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ms.add_bbox_xmax((0.47, 0.49), example)
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ms.add_bbox_point_x((0.47, 0.49), example)
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ms.add_bbox_point_y((0.47, 0.49), example)
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ms.add_bbox_3d_point_x((0.47, 0.49), example)
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ms.add_bbox_3d_point_y((0.47, 0.49), example)
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ms.add_bbox_3d_point_z((0.47, 0.49), example)
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ms.add_predicted_bbox_ymin((0.47, 0.49), example)
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ms.add_predicted_bbox_xmin((0.47, 0.49), example)
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ms.add_predicted_bbox_ymax((0.47, 0.49), example)
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ms.add_predicted_bbox_xmax((0.47, 0.49), example)
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ms.add_bbox_num_regions(47, example)
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ms.add_bbox_is_annotated(47, example)
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ms.add_bbox_is_generated((47, 49), example)
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ms.add_bbox_is_occluded((47, 49), example)
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ms.add_bbox_label_index((47, 49), example)
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ms.add_bbox_label_string((b"test", b"strings"), example)
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ms.add_bbox_label_confidence((0.47, 0.49), example)
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ms.add_bbox_class_index((47, 49), example)
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ms.add_bbox_class_string((b"test", b"strings"), example)
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ms.add_bbox_class_confidence((0.47, 0.49), example)
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ms.add_bbox_track_index((47, 49), example)
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ms.add_bbox_track_string((b"test", b"strings"), example)
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ms.add_bbox_track_confidence((0.47, 0.49), example)
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ms.add_bbox_timestamp(47, example)
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ms.add_predicted_bbox_class_index((47, 49), example)
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ms.add_predicted_bbox_class_string((b"test", b"strings"), example)
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ms.add_predicted_bbox_timestamp(47, example)
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ms.add_class_segmentation_encoded(b"test", example)
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ms.add_class_segmentation_multi_encoded([b"test", b"test"], example)
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ms.add_instance_segmentation_encoded(b"test", example)
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ms.add_instance_segmentation_multi_encoded([b"test", b"test"], example)
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ms.add_class_segmentation_timestamp(47, example)
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ms.set_bbox_embedding_dimensions_per_region((47, 49), example)
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ms.set_bbox_embedding_format(b"test", example)
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ms.add_bbox_embedding_floats((0.47, 0.49), example)
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ms.add_bbox_embedding_encoded((b"text", b"stings"), example)
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ms.add_bbox_embedding_confidence((0.47, 0.49), example)
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ms.set_text_language(b"test", example)
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ms.set_text_context_content(b"text", example)
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ms.add_text_content(b"one", example)
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ms.add_text_timestamp(47, example)
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ms.add_text_confidence(0.47, example)
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ms.add_text_duration(47, example)
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ms.add_text_token_id(47, example)
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ms.add_text_embedding((0.47, 0.49), example)
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def test_bbox_round_trip(self):
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example = tf.train.SequenceExample()
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boxes = np.array([[0.1, 0.2, 0.3, 0.4],
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[0.5, 0.6, 0.7, 0.8]])
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empty_boxes = np.array([])
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ms.add_bbox(boxes, example)
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ms.add_bbox(empty_boxes, example)
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self.assertEqual(2, ms.get_bbox_size(example))
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self.assertAllClose(boxes, ms.get_bbox_at(0, example))
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self.assertTrue(ms.has_bbox(example))
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ms.clear_bbox(example)
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self.assertEqual(0, ms.get_bbox_size(example))
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def test_point_round_trip(self):
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example = tf.train.SequenceExample()
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points = np.array([[0.1, 0.2],
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[0.5, 0.6]])
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ms.add_bbox_point(points, example)
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ms.add_bbox_point(points, example)
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self.assertEqual(2, ms.get_bbox_point_size(example))
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self.assertAllClose(points, ms.get_bbox_point_at(0, example))
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self.assertTrue(ms.has_bbox_point(example))
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ms.clear_bbox_point(example)
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self.assertEqual(0, ms.get_bbox_point_size(example))
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def test_prefixed_point_round_trip(self):
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example = tf.train.SequenceExample()
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points = np.array([[0.1, 0.2],
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[0.5, 0.6]])
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ms.add_bbox_point(points, example, "test")
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ms.add_bbox_point(points, example, "test")
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self.assertEqual(2, ms.get_bbox_point_size(example, "test"))
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self.assertAllClose(points, ms.get_bbox_point_at(0, example, "test"))
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self.assertTrue(ms.has_bbox_point(example, "test"))
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ms.clear_bbox_point(example, "test")
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self.assertEqual(0, ms.get_bbox_point_size(example, "test"))
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def test_3d_point_round_trip(self):
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example = tf.train.SequenceExample()
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points = np.array([[0.1, 0.2, 0.3],
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[0.5, 0.6, 0.7]])
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ms.add_bbox_3d_point(points, example)
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ms.add_bbox_3d_point(points, example)
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self.assertEqual(2, ms.get_bbox_3d_point_size(example))
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self.assertAllClose(points, ms.get_bbox_3d_point_at(0, example))
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self.assertTrue(ms.has_bbox_3d_point(example))
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ms.clear_bbox_3d_point(example)
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self.assertEqual(0, ms.get_bbox_3d_point_size(example))
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def test_predicted_bbox_round_trip(self):
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example = tf.train.SequenceExample()
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boxes = np.array([[0.1, 0.2, 0.3, 0.4],
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[0.5, 0.6, 0.7, 0.8]])
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ms.add_predicted_bbox(boxes, example)
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ms.add_predicted_bbox(boxes, example)
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self.assertEqual(2, ms.get_predicted_bbox_size(example))
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self.assertAllClose(boxes, ms.get_predicted_bbox_at(0, example))
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self.assertTrue(ms.has_predicted_bbox(example))
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ms.clear_predicted_bbox(example)
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self.assertEqual(0, ms.get_predicted_bbox_size(example))
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def test_float_list_round_trip(self):
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example = tf.train.SequenceExample()
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values_1 = [0.1, 0.2, 0.3]
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values_2 = [0.2, 0.3, 0.4]
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ms.add_feature_floats(values_1, example, "1")
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ms.add_feature_floats(values_1, example, "1")
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ms.add_feature_floats(values_2, example, "2")
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self.assertEqual(2, ms.get_feature_floats_size(example, "1"))
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self.assertEqual(1, ms.get_feature_floats_size(example, "2"))
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self.assertTrue(ms.has_feature_floats(example, "1"))
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self.assertTrue(ms.has_feature_floats(example, "2"))
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self.assertAllClose(values_1, ms.get_feature_floats_at(0, example, "1"))
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self.assertAllClose(values_2, ms.get_feature_floats_at(0, example, "2"))
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ms.clear_feature_floats(example, "1")
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self.assertEqual(0, ms.get_feature_floats_size(example, "1"))
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self.assertFalse(ms.has_feature_floats(example, "1"))
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self.assertEqual(1, ms.get_feature_floats_size(example, "2"))
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self.assertTrue(ms.has_feature_floats(example, "2"))
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ms.clear_feature_floats(example, "2")
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self.assertEqual(0, ms.get_feature_floats_size(example, "2"))
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self.assertFalse(ms.has_feature_floats(example, "2"))
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def test_feature_timestamp_round_trip(self):
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example = tf.train.SequenceExample()
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values_1 = 47
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values_2 = 49
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ms.add_feature_timestamp(values_1, example, "1")
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ms.add_feature_timestamp(values_1, example, "1")
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ms.add_feature_timestamp(values_2, example, "2")
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self.assertEqual(2, ms.get_feature_timestamp_size(example, "1"))
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self.assertEqual(1, ms.get_feature_timestamp_size(example, "2"))
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self.assertTrue(ms.has_feature_timestamp(example, "1"))
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self.assertTrue(ms.has_feature_timestamp(example, "2"))
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self.assertAllClose(values_1,
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ms.get_feature_timestamp_at(0, example, "1"))
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self.assertAllClose(values_2,
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ms.get_feature_timestamp_at(0, example, "2"))
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ms.clear_feature_timestamp(example, "1")
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self.assertEqual(0, ms.get_feature_timestamp_size(example, "1"))
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self.assertFalse(ms.has_feature_timestamp(example, "1"))
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self.assertEqual(1, ms.get_feature_timestamp_size(example, "2"))
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self.assertTrue(ms.has_feature_timestamp(example, "2"))
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ms.clear_feature_timestamp(example, "2")
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self.assertEqual(0, ms.get_feature_timestamp_size(example, "2"))
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self.assertFalse(ms.has_feature_timestamp(example, "2"))
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def test_feature_dimensions_round_trip(self):
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example = tf.train.SequenceExample()
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ms.set_feature_dimensions([47, 49], example, "1")
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ms.set_feature_dimensions([49, 50], example, "2")
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self.assertSequenceEqual([47, 49],
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ms.get_feature_dimensions(example, "1"))
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self.assertSequenceEqual([49, 50],
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ms.get_feature_dimensions(example, "2"))
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self.assertTrue(ms.has_feature_dimensions(example, "1"))
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self.assertTrue(ms.has_feature_dimensions(example, "2"))
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ms.clear_feature_dimensions(example, "1")
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self.assertFalse(ms.has_feature_dimensions(example, "1"))
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self.assertTrue(ms.has_feature_dimensions(example, "2"))
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ms.clear_feature_dimensions(example, "2")
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self.assertFalse(ms.has_feature_dimensions(example, "1"))
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self.assertFalse(ms.has_feature_dimensions(example, "2"))
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if __name__ == "__main__":
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tf.test.main()
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