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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.
# ==============================================================================
"""Numpy BoxMaskList classes and functions."""
import numpy as np
from object_detection.utils import np_box_list
class BoxMaskList(np_box_list.BoxList):
"""Convenience wrapper for BoxList with masks.
BoxMaskList extends the np_box_list.BoxList to contain masks as well.
In particular, its constructor receives both boxes and masks. Note that the
masks correspond to the full image.
"""
def __init__(self, box_data, mask_data):
"""Constructs box collection.
Args:
box_data: a numpy array of shape [N, 4] representing box coordinates
mask_data: a numpy array of shape [N, height, width] representing masks
with values are in {0,1}. The masks correspond to the full
image. The height and the width will be equal to image height and width.
Raises:
ValueError: if bbox data is not a numpy array
ValueError: if invalid dimensions for bbox data
ValueError: if mask data is not a numpy array
ValueError: if invalid dimension for mask data
"""
super(BoxMaskList, self).__init__(box_data)
if not isinstance(mask_data, np.ndarray):
raise ValueError('Mask data must be a numpy array.')
if len(mask_data.shape) != 3:
raise ValueError('Invalid dimensions for mask data.')
if mask_data.dtype != np.uint8:
raise ValueError('Invalid data type for mask data: uint8 is required.')
if mask_data.shape[0] != box_data.shape[0]:
raise ValueError('There should be the same number of boxes and masks.')
self.data['masks'] = mask_data
def get_masks(self):
"""Convenience function for accessing masks.
Returns:
a numpy array of shape [N, height, width] representing masks
"""
return self.get_field('masks')
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