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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for oid_tfrecord_creation.py."""
import pandas as pd
import tensorflow as tf
from object_detection.dataset_tools import oid_tfrecord_creation
def create_test_data():
data = {
'ImageID': ['i1', 'i1', 'i1', 'i1', 'i1', 'i2', 'i2'],
'LabelName': ['a', 'a', 'b', 'b', 'c', 'b', 'c'],
'YMin': [0.3, 0.6, 0.8, 0.1, None, 0.0, 0.0],
'XMin': [0.1, 0.3, 0.7, 0.0, None, 0.1, 0.1],
'XMax': [0.2, 0.3, 0.8, 0.5, None, 0.9, 0.9],
'YMax': [0.3, 0.6, 1, 0.8, None, 0.8, 0.8],
'IsOccluded': [0, 1, 1, 0, None, 0, 0],
'IsTruncated': [0, 0, 0, 1, None, 0, 0],
'IsGroupOf': [0, 0, 0, 0, None, 0, 1],
'IsDepiction': [1, 0, 0, 0, None, 0, 0],
'ConfidenceImageLabel': [None, None, None, None, 0, None, None],
}
df = pd.DataFrame(data=data)
label_map = {'a': 0, 'b': 1, 'c': 2}
return label_map, df
class TfExampleFromAnnotationsDataFrameTests(tf.test.TestCase):
def test_simple(self):
label_map, df = create_test_data()
tf_example = oid_tfrecord_creation.tf_example_from_annotations_data_frame(
df[df.ImageID == 'i1'], label_map, 'encoded_image_test')
self.assertProtoEquals(
"""
features {
feature {
key: "image/encoded"
value { bytes_list { value: "encoded_image_test" } } }
feature {
key: "image/filename"
value { bytes_list { value: "i1.jpg" } } }
feature {
key: "image/object/bbox/ymin"
value { float_list { value: [0.3, 0.6, 0.8, 0.1] } } }
feature {
key: "image/object/bbox/xmin"
value { float_list { value: [0.1, 0.3, 0.7, 0.0] } } }
feature {
key: "image/object/bbox/ymax"
value { float_list { value: [0.3, 0.6, 1.0, 0.8] } } }
feature {
key: "image/object/bbox/xmax"
value { float_list { value: [0.2, 0.3, 0.8, 0.5] } } }
feature {
key: "image/object/class/label"
value { int64_list { value: [0, 0, 1, 1] } } }
feature {
key: "image/object/class/text"
value { bytes_list { value: ["a", "a", "b", "b"] } } }
feature {
key: "image/source_id"
value { bytes_list { value: "i1" } } }
feature {
key: "image/object/depiction"
value { int64_list { value: [1, 0, 0, 0] } } }
feature {
key: "image/object/group_of"
value { int64_list { value: [0, 0, 0, 0] } } }
feature {
key: "image/object/occluded"
value { int64_list { value: [0, 1, 1, 0] } } }
feature {
key: "image/object/truncated"
value { int64_list { value: [0, 0, 0, 1] } } }
feature {
key: "image/class/label"
value { int64_list { value: [2] } } }
feature {
key: "image/class/text"
value { bytes_list { value: ["c"] } } } }
""", tf_example)
def test_no_attributes(self):
label_map, df = create_test_data()
del df['IsDepiction']
del df['IsGroupOf']
del df['IsOccluded']
del df['IsTruncated']
del df['ConfidenceImageLabel']
tf_example = oid_tfrecord_creation.tf_example_from_annotations_data_frame(
df[df.ImageID == 'i2'], label_map, 'encoded_image_test')
self.assertProtoEquals("""
features {
feature {
key: "image/encoded"
value { bytes_list { value: "encoded_image_test" } } }
feature {
key: "image/filename"
value { bytes_list { value: "i2.jpg" } } }
feature {
key: "image/object/bbox/ymin"
value { float_list { value: [0.0, 0.0] } } }
feature {
key: "image/object/bbox/xmin"
value { float_list { value: [0.1, 0.1] } } }
feature {
key: "image/object/bbox/ymax"
value { float_list { value: [0.8, 0.8] } } }
feature {
key: "image/object/bbox/xmax"
value { float_list { value: [0.9, 0.9] } } }
feature {
key: "image/object/class/label"
value { int64_list { value: [1, 2] } } }
feature {
key: "image/object/class/text"
value { bytes_list { value: ["b", "c"] } } }
feature {
key: "image/source_id"
value { bytes_list { value: "i2" } } } }
""", tf_example)
def test_label_filtering(self):
label_map, df = create_test_data()
label_map = {'a': 0}
tf_example = oid_tfrecord_creation.tf_example_from_annotations_data_frame(
df[df.ImageID == 'i1'], label_map, 'encoded_image_test')
self.assertProtoEquals(
"""
features {
feature {
key: "image/encoded"
value { bytes_list { value: "encoded_image_test" } } }
feature {
key: "image/filename"
value { bytes_list { value: "i1.jpg" } } }
feature {
key: "image/object/bbox/ymin"
value { float_list { value: [0.3, 0.6] } } }
feature {
key: "image/object/bbox/xmin"
value { float_list { value: [0.1, 0.3] } } }
feature {
key: "image/object/bbox/ymax"
value { float_list { value: [0.3, 0.6] } } }
feature {
key: "image/object/bbox/xmax"
value { float_list { value: [0.2, 0.3] } } }
feature {
key: "image/object/class/label"
value { int64_list { value: [0, 0] } } }
feature {
key: "image/object/class/text"
value { bytes_list { value: ["a", "a"] } } }
feature {
key: "image/source_id"
value { bytes_list { value: "i1" } } }
feature {
key: "image/object/depiction"
value { int64_list { value: [1, 0] } } }
feature {
key: "image/object/group_of"
value { int64_list { value: [0, 0] } } }
feature {
key: "image/object/occluded"
value { int64_list { value: [0, 1] } } }
feature {
key: "image/object/truncated"
value { int64_list { value: [0, 0] } } }
feature {
key: "image/class/label"
value { int64_list { } } }
feature {
key: "image/class/text"
value { bytes_list { } } } }
""", tf_example)
if __name__ == '__main__':
tf.test.main()
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