DR-App / object_detection /data_decoders /tf_example_decoder_test.py
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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 object_detection.data_decoders.tf_example_decoder."""
import os
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import test_util
from object_detection.core import standard_fields as fields
from object_detection.data_decoders import tf_example_decoder
from object_detection.protos import input_reader_pb2
from object_detection.utils import dataset_util
slim_example_decoder = tf.contrib.slim.tfexample_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 testDecodeAdditionalChannels(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
additional_channel_tensor = np.random.randint(
256, size=(4, 5, 1)).astype(np.uint8)
encoded_additional_channel = self._EncodeImage(additional_channel_tensor)
decoded_additional_channel = self._DecodeImage(encoded_additional_channel)
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/additional_channels/encoded':
dataset_util.bytes_list_feature(
[encoded_additional_channel] * 2),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/source_id':
dataset_util.bytes_feature('image_id'),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder(
num_additional_channels=2)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(
np.concatenate([decoded_additional_channel] * 2, axis=2),
tensor_dict[fields.InputDataFields.image_additional_channels])
def testDecodeJpegImage(self):
image_tensor = np.random.randint(256, 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': dataset_util.bytes_feature(encoded_jpeg),
'image/format': dataset_util.bytes_feature('jpeg'),
'image/source_id': dataset_util.bytes_feature('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])
self.assertAllEqual((tensor_dict[fields.InputDataFields.
original_image_spatial_shape].
get_shape().as_list()), [2])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(decoded_jpeg, tensor_dict[fields.InputDataFields.image])
self.assertAllEqual([4, 5], tensor_dict[fields.InputDataFields.
original_image_spatial_shape])
self.assertEqual('image_id', tensor_dict[fields.InputDataFields.source_id])
def testDecodeImageKeyAndFilename(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
'image/key/sha256': dataset_util.bytes_feature('abc'),
'image/filename': dataset_util.bytes_feature('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(256, 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': dataset_util.bytes_feature(encoded_png),
'image/format': dataset_util.bytes_feature('png'),
'image/source_id': dataset_util.bytes_feature('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])
self.assertAllEqual((tensor_dict[fields.InputDataFields.
original_image_spatial_shape].
get_shape().as_list()), [2])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(decoded_png, tensor_dict[fields.InputDataFields.image])
self.assertAllEqual([4, 5], tensor_dict[fields.InputDataFields.
original_image_spatial_shape])
self.assertEqual('image_id', tensor_dict[fields.InputDataFields.source_id])
def testDecodePngInstanceMasks(self):
image_tensor = np.random.randint(256, size=(10, 10, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
mask_1 = np.random.randint(0, 2, size=(10, 10, 1)).astype(np.uint8)
mask_2 = np.random.randint(0, 2, size=(10, 10, 1)).astype(np.uint8)
encoded_png_1 = self._EncodeImage(mask_1, encoding_type='png')
decoded_png_1 = np.squeeze(mask_1.astype(np.float32))
encoded_png_2 = self._EncodeImage(mask_2, encoding_type='png')
decoded_png_2 = np.squeeze(mask_2.astype(np.float32))
encoded_masks = [encoded_png_1, encoded_png_2]
decoded_masks = np.stack([decoded_png_1, decoded_png_2])
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/object/mask':
dataset_util.bytes_list_feature(encoded_masks)
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder(
load_instance_masks=True, instance_mask_type=input_reader_pb2.PNG_MASKS)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(
decoded_masks,
tensor_dict[fields.InputDataFields.groundtruth_instance_masks])
def testDecodeEmptyPngInstanceMasks(self):
image_tensor = np.random.randint(256, size=(10, 10, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
encoded_masks = []
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/object/mask':
dataset_util.bytes_list_feature(encoded_masks),
'image/height':
dataset_util.int64_feature(10),
'image/width':
dataset_util.int64_feature(10),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder(
load_instance_masks=True, instance_mask_type=input_reader_pb2.PNG_MASKS)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(
tensor_dict[fields.InputDataFields.groundtruth_instance_masks].shape,
[0, 10, 10])
def testDecodeBoundingBox(self):
image_tensor = np.random.randint(256, 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':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/object/bbox/ymin':
dataset_util.float_list_feature(bbox_ymins),
'image/object/bbox/xmin':
dataset_util.float_list_feature(bbox_xmins),
'image/object/bbox/ymax':
dataset_util.float_list_feature(bbox_ymaxs),
'image/object/bbox/xmax':
dataset_util.float_list_feature(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])
@test_util.enable_c_shapes
def testDecodeKeypoint(self):
image_tensor = np.random.randint(256, 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]
keypoint_ys = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
keypoint_xs = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/object/bbox/ymin':
dataset_util.float_list_feature(bbox_ymins),
'image/object/bbox/xmin':
dataset_util.float_list_feature(bbox_xmins),
'image/object/bbox/ymax':
dataset_util.float_list_feature(bbox_ymaxs),
'image/object/bbox/xmax':
dataset_util.float_list_feature(bbox_xmaxs),
'image/object/keypoint/y':
dataset_util.float_list_feature(keypoint_ys),
'image/object/keypoint/x':
dataset_util.float_list_feature(keypoint_xs),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder(num_keypoints=3)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_boxes]
.get_shape().as_list()), [None, 4])
self.assertAllEqual(
(tensor_dict[fields.InputDataFields.groundtruth_keypoints].get_shape()
.as_list()), [2, 3, 2])
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])
expected_keypoints = (
np.vstack([keypoint_ys, keypoint_xs]).transpose().reshape((2, 3, 2)))
self.assertAllEqual(
expected_keypoints,
tensor_dict[fields.InputDataFields.groundtruth_keypoints])
def testDecodeDefaultGroundtruthWeights(self):
image_tensor = np.random.randint(256, 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':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/object/bbox/ymin':
dataset_util.float_list_feature(bbox_ymins),
'image/object/bbox/xmin':
dataset_util.float_list_feature(bbox_xmins),
'image/object/bbox/ymax':
dataset_util.float_list_feature(bbox_ymaxs),
'image/object/bbox/xmax':
dataset_util.float_list_feature(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)
self.assertAllClose(tensor_dict[fields.InputDataFields.groundtruth_weights],
np.ones(2, dtype=np.float32))
@test_util.enable_c_shapes
def testDecodeObjectLabel(self):
image_tensor = np.random.randint(256, 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':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/object/class/label':
dataset_util.int64_list_feature(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()), [2])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(bbox_classes,
tensor_dict[fields.InputDataFields.groundtruth_classes])
def testDecodeMultiClassScores(self):
image_tensor = np.random.randint(256, 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]
flattened_multiclass_scores = [100., 50.] + [20., 30.]
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/object/class/multiclass_scores':
dataset_util.float_list_feature(flattened_multiclass_scores
),
'image/object/bbox/ymin':
dataset_util.float_list_feature(bbox_ymins),
'image/object/bbox/xmin':
dataset_util.float_list_feature(bbox_xmins),
'image/object/bbox/ymax':
dataset_util.float_list_feature(bbox_ymaxs),
'image/object/bbox/xmax':
dataset_util.float_list_feature(bbox_xmaxs),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder(
load_multiclass_scores=True)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(flattened_multiclass_scores,
tensor_dict[fields.InputDataFields.multiclass_scores])
def testDecodeObjectLabelNoText(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
bbox_classes = [1, 2]
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/object/class/label':
dataset_util.int64_list_feature(bbox_classes),
})).SerializeToString()
label_map_string = """
item {
id:1
name:'cat'
}
item {
id:2
name:'dog'
}
"""
label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
with tf.gfile.Open(label_map_path, 'wb') as f:
f.write(label_map_string)
example_decoder = tf_example_decoder.TfExampleDecoder(
label_map_proto_file=label_map_path)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_classes]
.get_shape().as_list()), [None])
init = tf.tables_initializer()
with self.test_session() as sess:
sess.run(init)
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(bbox_classes,
tensor_dict[fields.InputDataFields.groundtruth_classes])
def testDecodeObjectLabelWithText(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
bbox_classes_text = ['cat', 'dog']
# Annotation label gets overridden by labelmap id.
annotated_bbox_classes = [3, 4]
expected_bbox_classes = [1, 2]
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/object/class/text':
dataset_util.bytes_list_feature(bbox_classes_text),
'image/object/class/label':
dataset_util.int64_list_feature(annotated_bbox_classes),
})).SerializeToString()
label_map_string = """
item {
id:1
name:'cat'
}
item {
id:2
name:'dog'
}
"""
label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
with tf.gfile.Open(label_map_path, 'wb') as f:
f.write(label_map_string)
example_decoder = tf_example_decoder.TfExampleDecoder(
label_map_proto_file=label_map_path)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
init = tf.tables_initializer()
with self.test_session() as sess:
sess.run(init)
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(expected_bbox_classes,
tensor_dict[fields.InputDataFields.groundtruth_classes])
def testDecodeObjectLabelUnrecognizedName(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
bbox_classes_text = ['cat', 'cheetah']
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/object/class/text':
dataset_util.bytes_list_feature(bbox_classes_text),
})).SerializeToString()
label_map_string = """
item {
id:2
name:'cat'
}
item {
id:1
name:'dog'
}
"""
label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
with tf.gfile.Open(label_map_path, 'wb') as f:
f.write(label_map_string)
example_decoder = tf_example_decoder.TfExampleDecoder(
label_map_proto_file=label_map_path)
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:
sess.run(tf.tables_initializer())
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual([2, -1],
tensor_dict[fields.InputDataFields.groundtruth_classes])
def testDecodeObjectLabelWithMappingWithDisplayName(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
bbox_classes_text = ['cat', 'dog']
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/object/class/text':
dataset_util.bytes_list_feature(bbox_classes_text),
})).SerializeToString()
label_map_string = """
item {
id:3
display_name:'cat'
}
item {
id:1
display_name:'dog'
}
"""
label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
with tf.gfile.Open(label_map_path, 'wb') as f:
f.write(label_map_string)
example_decoder = tf_example_decoder.TfExampleDecoder(
label_map_proto_file=label_map_path)
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:
sess.run(tf.tables_initializer())
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual([3, 1],
tensor_dict[fields.InputDataFields.groundtruth_classes])
def testDecodeObjectLabelUnrecognizedNameWithMappingWithDisplayName(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
bbox_classes_text = ['cat', 'cheetah']
bbox_classes_id = [5, 6]
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/object/class/text':
dataset_util.bytes_list_feature(bbox_classes_text),
'image/object/class/label':
dataset_util.int64_list_feature(bbox_classes_id),
})).SerializeToString()
label_map_string = """
item {
name:'/m/cat'
id:3
display_name:'cat'
}
item {
name:'/m/dog'
id:1
display_name:'dog'
}
"""
label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
with tf.gfile.Open(label_map_path, 'wb') as f:
f.write(label_map_string)
example_decoder = tf_example_decoder.TfExampleDecoder(
label_map_proto_file=label_map_path)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
with self.test_session() as sess:
sess.run(tf.tables_initializer())
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual([3, -1],
tensor_dict[fields.InputDataFields.groundtruth_classes])
def testDecodeObjectLabelWithMappingWithName(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
bbox_classes_text = ['cat', 'dog']
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/object/class/text':
dataset_util.bytes_list_feature(bbox_classes_text),
})).SerializeToString()
label_map_string = """
item {
id:3
name:'cat'
}
item {
id:1
name:'dog'
}
"""
label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
with tf.gfile.Open(label_map_path, 'wb') as f:
f.write(label_map_string)
example_decoder = tf_example_decoder.TfExampleDecoder(
label_map_proto_file=label_map_path)
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:
sess.run(tf.tables_initializer())
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual([3, 1],
tensor_dict[fields.InputDataFields.groundtruth_classes])
@test_util.enable_c_shapes
def testDecodeObjectArea(self):
image_tensor = np.random.randint(256, 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':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/object/area':
dataset_util.float_list_feature(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()), [2])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(object_area,
tensor_dict[fields.InputDataFields.groundtruth_area])
@test_util.enable_c_shapes
def testDecodeObjectIsCrowd(self):
image_tensor = np.random.randint(256, 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':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/object/is_crowd':
dataset_util.int64_list_feature(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()), [2])
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])
@test_util.enable_c_shapes
def testDecodeObjectDifficult(self):
image_tensor = np.random.randint(256, 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':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/object/difficult':
dataset_util.int64_list_feature(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()), [2])
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])
@test_util.enable_c_shapes
def testDecodeObjectGroupOf(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
object_group_of = [0, 1]
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/object/group_of':
dataset_util.int64_list_feature(object_group_of),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual(
(tensor_dict[fields.InputDataFields.groundtruth_group_of].get_shape()
.as_list()), [2])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(
[bool(item) for item in object_group_of],
tensor_dict[fields.InputDataFields.groundtruth_group_of])
def testDecodeObjectWeight(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
object_weights = [0.75, 1.0]
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/object/weight':
dataset_util.float_list_feature(object_weights),
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_weights]
.get_shape().as_list()), [None])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(object_weights,
tensor_dict[fields.InputDataFields.groundtruth_weights])
@test_util.enable_c_shapes
def testDecodeInstanceSegmentation(self):
num_instances = 4
image_height = 5
image_width = 3
# Randomly generate image.
image_tensor = np.random.randint(
256, size=(image_height, image_width, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
# Randomly generate instance segmentation masks.
instance_masks = (
np.random.randint(2, size=(num_instances, image_height,
image_width)).astype(np.float32))
instance_masks_flattened = np.reshape(instance_masks, [-1])
# Randomly generate class labels for each instance.
object_classes = np.random.randint(
100, size=(num_instances)).astype(np.int64)
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/height':
dataset_util.int64_feature(image_height),
'image/width':
dataset_util.int64_feature(image_width),
'image/object/mask':
dataset_util.float_list_feature(instance_masks_flattened),
'image/object/class/label':
dataset_util.int64_list_feature(object_classes)
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder(
load_instance_masks=True)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertAllEqual(
(tensor_dict[fields.InputDataFields.groundtruth_instance_masks]
.get_shape().as_list()), [4, 5, 3])
self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_classes]
.get_shape().as_list()), [4])
with self.test_session() as sess:
tensor_dict = sess.run(tensor_dict)
self.assertAllEqual(
instance_masks.astype(np.float32),
tensor_dict[fields.InputDataFields.groundtruth_instance_masks])
self.assertAllEqual(object_classes,
tensor_dict[fields.InputDataFields.groundtruth_classes])
def testInstancesNotAvailableByDefault(self):
num_instances = 4
image_height = 5
image_width = 3
# Randomly generate image.
image_tensor = np.random.randint(
256, size=(image_height, image_width, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
# Randomly generate instance segmentation masks.
instance_masks = (
np.random.randint(2, size=(num_instances, image_height,
image_width)).astype(np.float32))
instance_masks_flattened = np.reshape(instance_masks, [-1])
# Randomly generate class labels for each instance.
object_classes = np.random.randint(
100, size=(num_instances)).astype(np.int64)
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/height':
dataset_util.int64_feature(image_height),
'image/width':
dataset_util.int64_feature(image_width),
'image/object/mask':
dataset_util.float_list_feature(instance_masks_flattened),
'image/object/class/label':
dataset_util.int64_list_feature(object_classes)
})).SerializeToString()
example_decoder = tf_example_decoder.TfExampleDecoder()
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
self.assertTrue(
fields.InputDataFields.groundtruth_instance_masks not in tensor_dict)
def testDecodeImageLabels(self):
image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
encoded_jpeg = self._EncodeImage(image_tensor)
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded': dataset_util.bytes_feature(encoded_jpeg),
'image/format': dataset_util.bytes_feature('jpeg'),
'image/class/label': dataset_util.int64_list_feature([1, 2]),
})).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.assertTrue(
fields.InputDataFields.groundtruth_image_classes in tensor_dict)
self.assertAllEqual(
tensor_dict[fields.InputDataFields.groundtruth_image_classes],
np.array([1, 2]))
example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded':
dataset_util.bytes_feature(encoded_jpeg),
'image/format':
dataset_util.bytes_feature('jpeg'),
'image/class/text':
dataset_util.bytes_list_feature(['dog', 'cat']),
})).SerializeToString()
label_map_string = """
item {
id:3
name:'cat'
}
item {
id:1
name:'dog'
}
"""
label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
with tf.gfile.Open(label_map_path, 'wb') as f:
f.write(label_map_string)
example_decoder = tf_example_decoder.TfExampleDecoder(
label_map_proto_file=label_map_path)
tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))
with self.test_session() as sess:
sess.run(tf.tables_initializer())
tensor_dict = sess.run(tensor_dict)
self.assertTrue(
fields.InputDataFields.groundtruth_image_classes in tensor_dict)
self.assertAllEqual(
tensor_dict[fields.InputDataFields.groundtruth_image_classes],
np.array([1, 3]))
if __name__ == '__main__':
tf.test.main()