# 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 object_detection.data_decoders.tf_example_decoder."""
import numpy as np
import tensorflow as tf
from object_detection.core import standard_fields as fields
from object_detection.data_decoders import tf_example_decoder
class TfExampleDecoderTest(tf.test.TestCase):
def _EncodeImage(self, image_tensor, encoding_type='jpeg'):
with self.test_session():
if encoding_type == 'jpeg':
image_encoded = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
elif encoding_type == 'png':
image_encoded = tf.image.encode_png(tf.constant(image_tensor)).eval()
else:
raise ValueError('Invalid encoding type.')
return image_encoded
def _DecodeImage(self, image_encoded, encoding_type='jpeg'):
with self.test_session():
if encoding_type == 'jpeg':
image_decoded = tf.image.decode_jpeg(tf.constant(image_encoded)).eval()
elif encoding_type == 'png':
image_decoded = tf.image.decode_png(tf.constant(image_encoded)).eval()
else:
raise ValueError('Invalid encoding type.')
return image_decoded
def _Int64Feature(self, value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
def _FloatFeature(self, value):
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
def _BytesFeature(self, value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def testDecodeJpegImage(self):
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
decoded_jpeg = self._DecodeImage(encoded_jpeg)
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': self._BytesFeature(encoded_jpeg),
'image/format': self._BytesFeature('jpeg'),
'image/source_id': self._BytesFeature('image_id'),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.Decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[fields.InputDataFields.image].
get_shape().as_list()), [None, None, 3])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(decoded_jpeg, tensor_dict[fields.InputDataFields.image])
self.assertEqual('image_id', tensor_dict[fields.InputDataFields.source_id])
def testDecodeImageKeyAndFilename(self):
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': self._BytesFeature(encoded_jpeg),
'image/key/sha256': self._BytesFeature('abc'),
'image/filename': self._BytesFeature('filename')
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.Decode(tf.convert_to_tensor(example))
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertEqual('abc', tensor_dict[fields.InputDataFields.key])
self.assertEqual('filename', tensor_dict[fields.InputDataFields.filename])
def testDecodePngImage(self):
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
encoded_png = self._EncodeImage(image_tensor, encoding_type='png')
decoded_png = self._DecodeImage(encoded_png, encoding_type='png')
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': self._BytesFeature(encoded_png),
'image/format': self._BytesFeature('png'),
'image/source_id': self._BytesFeature('image_id')
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.Decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[fields.InputDataFields.image].
get_shape().as_list()), [None, None, 3])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(decoded_png, tensor_dict[fields.InputDataFields.image])
self.assertEqual('image_id', tensor_dict[fields.InputDataFields.source_id])
def testDecodeBoundingBox(self):
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
bbox_ymins = [0.0, 4.0]
bbox_xmins = [1.0, 5.0]
bbox_ymaxs = [2.0, 6.0]
bbox_xmaxs = [3.0, 7.0]
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': self._BytesFeature(encoded_jpeg),
'image/format': self._BytesFeature('jpeg'),
'image/object/bbox/ymin': self._FloatFeature(bbox_ymins),
'image/object/bbox/xmin': self._FloatFeature(bbox_xmins),
'image/object/bbox/ymax': self._FloatFeature(bbox_ymaxs),
'image/object/bbox/xmax': self._FloatFeature(bbox_xmaxs),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.Decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_boxes].
get_shape().as_list()), [None, 4])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
expected_boxes = np.vstack([bbox_ymins, bbox_xmins,
bbox_ymaxs, bbox_xmaxs]).transpose()
self.assertAllEqual(expected_boxes,
tensor_dict[fields.InputDataFields.groundtruth_boxes])
def testDecodeObjectLabel(self):
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
bbox_classes = [0, 1]
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': self._BytesFeature(encoded_jpeg),
'image/format': self._BytesFeature('jpeg'),
'image/object/class/label': self._Int64Feature(bbox_classes),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.Decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[
fields.InputDataFields.groundtruth_classes].get_shape().as_list()),
[None])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(bbox_classes,
tensor_dict[fields.InputDataFields.groundtruth_classes])
def testDecodeObjectArea(self):
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
object_area = [100., 174.]
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': self._BytesFeature(encoded_jpeg),
'image/format': self._BytesFeature('jpeg'),
'image/object/area': self._FloatFeature(object_area),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.Decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_area].
get_shape().as_list()), [None])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(object_area,
tensor_dict[fields.InputDataFields.groundtruth_area])
def testDecodeObjectIsCrowd(self):
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
object_is_crowd = [0, 1]
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': self._BytesFeature(encoded_jpeg),
'image/format': self._BytesFeature('jpeg'),
'image/object/is_crowd': self._Int64Feature(object_is_crowd),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.Decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[
fields.InputDataFields.groundtruth_is_crowd].get_shape().as_list()),
[None])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual([bool(item) for item in object_is_crowd],
tensor_dict[
fields.InputDataFields.groundtruth_is_crowd])
def testDecodeObjectDifficult(self):
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
object_difficult = [0, 1]
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': self._BytesFeature(encoded_jpeg),
'image/format': self._BytesFeature('jpeg'),
'image/object/difficult': self._Int64Feature(object_difficult),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.Decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[
fields.InputDataFields.groundtruth_difficult].get_shape().as_list()),
[None])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual([bool(item) for item in object_difficult],
tensor_dict[
fields.InputDataFields.groundtruth_difficult])
def testDecodeInstanceSegmentation(self):
num_instances = 4
image_height = 5
image_width = 3
# Randomly generate image.
image_tensor = np.random.randint(255, size=(image_height,
image_width,
3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
# Randomly generate instance segmentation masks.
instance_segmentation = (
np.random.randint(2, size=(num_instances,
image_height,
image_width)).astype(np.int64))
# Randomly generate class labels for each instance.
instance_segmentation_classes = np.random.randint(
100, size=(num_instances)).astype(np.int64)
example = tf.train.Example(features=tf.train.Features(feature={
'image/encoded': self._BytesFeature(encoded_jpeg),
'image/format': self._BytesFeature('jpeg'),
'image/height': self._Int64Feature([image_height]),
'image/width': self._Int64Feature([image_width]),
'image/segmentation/object': self._Int64Feature(
instance_segmentation.flatten()),
'image/segmentation/object/class': self._Int64Feature(
instance_segmentation_classes)})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.Decode(tf.convert_to_tensor(example))
self.assertAllEqual((
tensor_dict[fields.InputDataFields.groundtruth_instance_masks].
get_shape().as_list()), [None, None, None])
self.assertAllEqual((
tensor_dict[fields.InputDataFields.groundtruth_instance_classes].
get_shape().as_list()), [None])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(
instance_segmentation.astype(np.bool),
tensor_dict[fields.InputDataFields.groundtruth_instance_masks])
self.assertAllEqual(
instance_segmentation_classes,
tensor_dict[fields.InputDataFields.groundtruth_instance_classes])
if __name__ == '__main__':
tf.test.main()
