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| # Lint as: python2, python3 | |
| # Copyright 2019 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. | |
| # ============================================================================== | |
| """Utility functions to set up unit tests on Panoptic Segmentation code.""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import os | |
| from absl import flags | |
| import numpy as np | |
| import scipy.misc | |
| import six | |
| from six.moves import map | |
| FLAGS = flags.FLAGS | |
| _TEST_DIR = 'deeplab/evaluation/testdata' | |
| def read_test_image(testdata_path, *args, **kwargs): | |
| """Loads a test image. | |
| Args: | |
| testdata_path: Image path relative to panoptic_segmentation/testdata as a | |
| string. | |
| *args: Additional positional arguments passed to `imread`. | |
| **kwargs: Additional keyword arguments passed to `imread`. | |
| Returns: | |
| The image, as a numpy array. | |
| """ | |
| image_path = os.path.join(_TEST_DIR, testdata_path) | |
| return scipy.misc.imread(image_path, *args, **kwargs) | |
| def read_segmentation_with_rgb_color_map(image_testdata_path, | |
| rgb_to_semantic_label, | |
| output_dtype=None): | |
| """Reads a test segmentation as an image and a map from colors to labels. | |
| Args: | |
| image_testdata_path: Image path relative to panoptic_segmentation/testdata | |
| as a string. | |
| rgb_to_semantic_label: Mapping from RGB colors to integer labels as a | |
| dictionary. | |
| output_dtype: Type of the output labels. If None, defaults to the type of | |
| the provided color map. | |
| Returns: | |
| A 2D numpy array of labels. | |
| Raises: | |
| ValueError: On an incomplete `rgb_to_semantic_label`. | |
| """ | |
| rgb_image = read_test_image(image_testdata_path, mode='RGB') | |
| if len(rgb_image.shape) != 3 or rgb_image.shape[2] != 3: | |
| raise AssertionError( | |
| 'Expected RGB image, actual shape is %s' % rgb_image.sape) | |
| num_pixels = rgb_image.shape[0] * rgb_image.shape[1] | |
| unique_colors = np.unique(np.reshape(rgb_image, [num_pixels, 3]), axis=0) | |
| if not set(map(tuple, unique_colors)).issubset( | |
| six.viewkeys(rgb_to_semantic_label)): | |
| raise ValueError('RGB image has colors not in color map.') | |
| output_dtype = output_dtype or type( | |
| next(six.itervalues(rgb_to_semantic_label))) | |
| output_labels = np.empty(rgb_image.shape[:2], dtype=output_dtype) | |
| for rgb_color, int_label in six.iteritems(rgb_to_semantic_label): | |
| color_array = np.array(rgb_color, ndmin=3) | |
| output_labels[np.all(rgb_image == color_array, axis=2)] = int_label | |
| return output_labels | |
| def panoptic_segmentation_with_class_map(instance_testdata_path, | |
| instance_label_to_semantic_label): | |
| """Reads in a panoptic segmentation with an instance map and a map to classes. | |
| Args: | |
| instance_testdata_path: Path to a grayscale instance map, given as a string | |
| and relative to panoptic_segmentation/testdata. | |
| instance_label_to_semantic_label: A map from instance labels to class | |
| labels. | |
| Returns: | |
| A tuple `(instance_labels, class_labels)` of numpy arrays. | |
| Raises: | |
| ValueError: On a mismatched set of instances in | |
| the | |
| `instance_label_to_semantic_label`. | |
| """ | |
| instance_labels = read_test_image(instance_testdata_path, mode='L') | |
| if set(np.unique(instance_labels)) != set( | |
| six.iterkeys(instance_label_to_semantic_label)): | |
| raise ValueError('Provided class map does not match present instance ids.') | |
| class_labels = np.empty_like(instance_labels) | |
| for instance_id, class_id in six.iteritems(instance_label_to_semantic_label): | |
| class_labels[instance_labels == instance_id] = class_id | |
| return instance_labels, class_labels | |