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63 lines
2.4 KiB
63 lines
2.4 KiB
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Numpy BoxMaskList classes and functions."""
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import numpy as np
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from object_detection.utils import np_box_list
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class BoxMaskList(np_box_list.BoxList):
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"""Convenience wrapper for BoxList with masks.
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BoxMaskList extends the np_box_list.BoxList to contain masks as well.
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In particular, its constructor receives both boxes and masks. Note that the
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masks correspond to the full image.
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"""
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def __init__(self, box_data, mask_data):
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"""Constructs box collection.
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Args:
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box_data: a numpy array of shape [N, 4] representing box coordinates
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mask_data: a numpy array of shape [N, height, width] representing masks
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with values are in {0,1}. The masks correspond to the full
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image. The height and the width will be equal to image height and width.
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Raises:
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ValueError: if bbox data is not a numpy array
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ValueError: if invalid dimensions for bbox data
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ValueError: if mask data is not a numpy array
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ValueError: if invalid dimension for mask data
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"""
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super(BoxMaskList, self).__init__(box_data)
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if not isinstance(mask_data, np.ndarray):
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raise ValueError('Mask data must be a numpy array.')
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if len(mask_data.shape) != 3:
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raise ValueError('Invalid dimensions for mask data.')
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if mask_data.dtype != np.uint8:
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raise ValueError('Invalid data type for mask data: uint8 is required.')
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if mask_data.shape[0] != box_data.shape[0]:
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raise ValueError('There should be the same number of boxes and masks.')
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self.data['masks'] = mask_data
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def get_masks(self):
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"""Convenience function for accessing masks.
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Returns:
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a numpy array of shape [N, height, width] representing masks
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"""
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return self.get_field('masks')
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