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197 lines
7.6 KiB
197 lines
7.6 KiB
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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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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# ==============================================================================
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"""Tests for object_detection.data_decoders.tf_example_parser."""
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import numpy as np
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import numpy.testing as np_testing
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import tensorflow as tf
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from object_detection.core import standard_fields as fields
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from object_detection.metrics import tf_example_parser
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class TfExampleDecoderTest(tf.test.TestCase):
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def _Int64Feature(self, value):
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return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
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def _FloatFeature(self, value):
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return tf.train.Feature(float_list=tf.train.FloatList(value=value))
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def _BytesFeature(self, value):
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return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
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def testParseDetectionsAndGT(self):
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source_id = 'abc.jpg'
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# y_min, x_min, y_max, x_max
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object_bb = np.array([[0.0, 0.5, 0.3], [0.0, 0.1, 0.6], [1.0, 0.6, 0.8],
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[1.0, 0.6, 0.7]]).transpose()
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detection_bb = np.array([[0.1, 0.2], [0.0, 0.8], [1.0, 0.6],
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[1.0, 0.85]]).transpose()
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object_class_label = [1, 1, 2]
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object_difficult = [1, 0, 0]
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object_group_of = [0, 0, 1]
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verified_labels = [1, 2, 3, 4]
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detection_class_label = [2, 1]
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detection_score = [0.5, 0.3]
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features = {
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fields.TfExampleFields.source_id:
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self._BytesFeature(source_id),
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fields.TfExampleFields.object_bbox_ymin:
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self._FloatFeature(object_bb[:, 0].tolist()),
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fields.TfExampleFields.object_bbox_xmin:
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self._FloatFeature(object_bb[:, 1].tolist()),
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fields.TfExampleFields.object_bbox_ymax:
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self._FloatFeature(object_bb[:, 2].tolist()),
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fields.TfExampleFields.object_bbox_xmax:
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self._FloatFeature(object_bb[:, 3].tolist()),
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fields.TfExampleFields.detection_bbox_ymin:
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self._FloatFeature(detection_bb[:, 0].tolist()),
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fields.TfExampleFields.detection_bbox_xmin:
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self._FloatFeature(detection_bb[:, 1].tolist()),
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fields.TfExampleFields.detection_bbox_ymax:
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self._FloatFeature(detection_bb[:, 2].tolist()),
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fields.TfExampleFields.detection_bbox_xmax:
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self._FloatFeature(detection_bb[:, 3].tolist()),
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fields.TfExampleFields.detection_class_label:
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self._Int64Feature(detection_class_label),
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fields.TfExampleFields.detection_score:
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self._FloatFeature(detection_score),
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}
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example = tf.train.Example(features=tf.train.Features(feature=features))
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parser = tf_example_parser.TfExampleDetectionAndGTParser()
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results_dict = parser.parse(example)
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self.assertIsNone(results_dict)
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features[fields.TfExampleFields.object_class_label] = (
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self._Int64Feature(object_class_label))
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features[fields.TfExampleFields.object_difficult] = (
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self._Int64Feature(object_difficult))
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example = tf.train.Example(features=tf.train.Features(feature=features))
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results_dict = parser.parse(example)
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self.assertIsNotNone(results_dict)
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self.assertEqual(source_id, results_dict[fields.DetectionResultFields.key])
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np_testing.assert_almost_equal(
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object_bb, results_dict[fields.InputDataFields.groundtruth_boxes])
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np_testing.assert_almost_equal(
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detection_bb,
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results_dict[fields.DetectionResultFields.detection_boxes])
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np_testing.assert_almost_equal(
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detection_score,
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results_dict[fields.DetectionResultFields.detection_scores])
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np_testing.assert_almost_equal(
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detection_class_label,
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results_dict[fields.DetectionResultFields.detection_classes])
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np_testing.assert_almost_equal(
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object_difficult,
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results_dict[fields.InputDataFields.groundtruth_difficult])
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np_testing.assert_almost_equal(
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object_class_label,
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results_dict[fields.InputDataFields.groundtruth_classes])
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parser = tf_example_parser.TfExampleDetectionAndGTParser()
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features[fields.TfExampleFields.object_group_of] = (
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self._Int64Feature(object_group_of))
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example = tf.train.Example(features=tf.train.Features(feature=features))
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results_dict = parser.parse(example)
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self.assertIsNotNone(results_dict)
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np_testing.assert_equal(
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object_group_of,
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results_dict[fields.InputDataFields.groundtruth_group_of])
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features[fields.TfExampleFields.image_class_label] = (
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self._Int64Feature(verified_labels))
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example = tf.train.Example(features=tf.train.Features(feature=features))
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results_dict = parser.parse(example)
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self.assertIsNotNone(results_dict)
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np_testing.assert_equal(
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verified_labels,
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results_dict[fields.InputDataFields.groundtruth_image_classes])
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def testParseString(self):
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string_val = 'abc'
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features = {'string': self._BytesFeature(string_val)}
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example = tf.train.Example(features=tf.train.Features(feature=features))
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parser = tf_example_parser.StringParser('string')
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result = parser.parse(example)
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self.assertIsNotNone(result)
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self.assertEqual(result, string_val)
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parser = tf_example_parser.StringParser('another_string')
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result = parser.parse(example)
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self.assertIsNone(result)
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def testParseFloat(self):
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float_array_val = [1.5, 1.4, 2.0]
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features = {'floats': self._FloatFeature(float_array_val)}
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example = tf.train.Example(features=tf.train.Features(feature=features))
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parser = tf_example_parser.FloatParser('floats')
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result = parser.parse(example)
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self.assertIsNotNone(result)
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np_testing.assert_almost_equal(result, float_array_val)
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parser = tf_example_parser.StringParser('another_floats')
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result = parser.parse(example)
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self.assertIsNone(result)
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def testInt64Parser(self):
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int_val = [1, 2, 3]
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features = {'ints': self._Int64Feature(int_val)}
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example = tf.train.Example(features=tf.train.Features(feature=features))
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parser = tf_example_parser.Int64Parser('ints')
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result = parser.parse(example)
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self.assertIsNotNone(result)
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np_testing.assert_almost_equal(result, int_val)
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parser = tf_example_parser.Int64Parser('another_ints')
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result = parser.parse(example)
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self.assertIsNone(result)
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def testBoundingBoxParser(self):
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bounding_boxes = np.array([[0.0, 0.5, 0.3], [0.0, 0.1, 0.6],
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[1.0, 0.6, 0.8], [1.0, 0.6, 0.7]]).transpose()
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features = {
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'ymin': self._FloatFeature(bounding_boxes[:, 0]),
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'xmin': self._FloatFeature(bounding_boxes[:, 1]),
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'ymax': self._FloatFeature(bounding_boxes[:, 2]),
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'xmax': self._FloatFeature(bounding_boxes[:, 3])
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}
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example = tf.train.Example(features=tf.train.Features(feature=features))
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parser = tf_example_parser.BoundingBoxParser('xmin', 'ymin', 'xmax', 'ymax')
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result = parser.parse(example)
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self.assertIsNotNone(result)
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np_testing.assert_almost_equal(result, bounding_boxes)
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parser = tf_example_parser.BoundingBoxParser('xmin', 'ymin', 'xmax',
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'another_ymax')
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result = parser.parse(example)
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self.assertIsNone(result)
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if __name__ == '__main__':
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tf.test.main()
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