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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
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