# 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.
# ==============================================================================
"""Tensorflow Example proto decoder for object detection.
A decoder to decode string tensors containing serialized tensorflow.Example
protos for object detection.
"""
import tensorflow as tf
from object_detection.core import data_decoder
from object_detection.core import standard_fields as fields
slim_example_decoder = tf.contrib.slim.tfexample_decoder
class TfExampleDecoder(data_decoder.DataDecoder):
"""Tensorflow Example proto decoder."""
def __init__(self):
"""Constructor sets keys_to_features and items_to_handlers."""
self.keys_to_features = {
'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
'image/format': tf.FixedLenFeature((), tf.string, default_value='jpeg'),
'image/filename': tf.FixedLenFeature((), tf.string, default_value=''),
'image/key/sha256': tf.FixedLenFeature((), tf.string, default_value=''),
'image/source_id': tf.FixedLenFeature((), tf.string, default_value=''),
'image/height': tf.FixedLenFeature((), tf.int64, 1),
'image/width': tf.FixedLenFeature((), tf.int64, 1),
# Object boxes and classes.
'image/object/bbox/xmin': tf.VarLenFeature(tf.float32),
'image/object/bbox/xmax': tf.VarLenFeature(tf.float32),
'image/object/bbox/ymin': tf.VarLenFeature(tf.float32),
'image/object/bbox/ymax': tf.VarLenFeature(tf.float32),
'image/object/class/label': tf.VarLenFeature(tf.int64),
'image/object/area': tf.VarLenFeature(tf.float32),
'image/object/is_crowd': tf.VarLenFeature(tf.int64),
'image/object/difficult': tf.VarLenFeature(tf.int64),
# Instance masks and classes.
'image/segmentation/object': tf.VarLenFeature(tf.int64),
'image/segmentation/object/class': tf.VarLenFeature(tf.int64)
}
self.items_to_handlers = {
fields.InputDataFields.image: slim_example_decoder.Image(
image_key='image/encoded', format_key='image/format', channels=3),
fields.InputDataFields.source_id: (
slim_example_decoder.Tensor('image/source_id')),
fields.InputDataFields.key: (
slim_example_decoder.Tensor('image/key/sha256')),
fields.InputDataFields.filename: (
slim_example_decoder.Tensor('image/filename')),
# Object boxes and classes.
fields.InputDataFields.groundtruth_boxes: (
slim_example_decoder.BoundingBox(
['ymin', 'xmin', 'ymax', 'xmax'], 'image/object/bbox/')),
fields.InputDataFields.groundtruth_classes: (
slim_example_decoder.Tensor('image/object/class/label')),
fields.InputDataFields.groundtruth_area: slim_example_decoder.Tensor(
'image/object/area'),
fields.InputDataFields.groundtruth_is_crowd: (
slim_example_decoder.Tensor('image/object/is_crowd')),
fields.InputDataFields.groundtruth_difficult: (
slim_example_decoder.Tensor('image/object/difficult')),
# Instance masks and classes.
fields.InputDataFields.groundtruth_instance_masks: (
slim_example_decoder.ItemHandlerCallback(
['image/segmentation/object', 'image/height', 'image/width'],
self._reshape_instance_masks)),
fields.InputDataFields.groundtruth_instance_classes: (
slim_example_decoder.Tensor('image/segmentation/object/class')),
}
def Decode(self, tf_example_string_tensor):
"""Decodes serialized tensorflow example and returns a tensor dictionary.
Args:
tf_example_string_tensor: a string tensor holding a serialized tensorflow
example proto.
Returns:
A dictionary of the following tensors.
fields.InputDataFields.image - 3D uint8 tensor of shape [None, None, 3]
containing image.
fields.InputDataFields.source_id - string tensor containing original
image id.
fields.InputDataFields.key - string tensor with unique sha256 hash key.
fields.InputDataFields.filename - string tensor with original dataset
filename.
fields.InputDataFields.groundtruth_boxes - 2D float32 tensor of shape
[None, 4] containing box corners.
fields.InputDataFields.groundtruth_classes - 1D int64 tensor of shape
[None] containing classes for the boxes.
fields.InputDataFields.groundtruth_area - 1D float32 tensor of shape
[None] containing containing object mask area in pixel squared.
fields.InputDataFields.groundtruth_is_crowd - 1D bool tensor of shape
[None] indicating if the boxes enclose a crowd.
fields.InputDataFields.groundtruth_difficult - 1D bool tensor of shape
[None] indicating if the boxes represent `difficult` instances.
fields.InputDataFields.groundtruth_instance_masks - 3D int64 tensor of
shape [None, None, None] containing instance masks.
fields.InputDataFields.groundtruth_instance_classes - 1D int64 tensor
of shape [None] containing classes for the instance masks.
"""
serialized_example = tf.reshape(tf_example_string_tensor, shape=[])
decoder = slim_example_decoder.TFExampleDecoder(self.keys_to_features,
self.items_to_handlers)
keys = decoder.list_items()
tensors = decoder.decode(serialized_example, items=keys)
tensor_dict = dict(zip(keys, tensors))
is_crowd = fields.InputDataFields.groundtruth_is_crowd
tensor_dict[is_crowd] = tf.cast(tensor_dict[is_crowd], dtype=tf.bool)
tensor_dict[fields.InputDataFields.image].set_shape([None, None, 3])
return tensor_dict
def _reshape_instance_masks(self, keys_to_tensors):
"""Reshape instance segmentation masks.
The instance segmentation masks are reshaped to [num_instances, height,
width] and cast to boolean type to save memory.
Args:
keys_to_tensors: a dictionary from keys to tensors.
Returns:
A 3-D boolean tensor of shape [num_instances, height, width].
"""
masks = keys_to_tensors['image/segmentation/object']
if isinstance(masks, tf.SparseTensor):
masks = tf.sparse_tensor_to_dense(masks)
height = keys_to_tensors['image/height']
width = keys_to_tensors['image/width']
to_shape = tf.cast(tf.stack([-1, height, width]), tf.int32)
return tf.cast(tf.reshape(masks, to_shape), tf.bool)
