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# Copyright 2015 Google Inc. 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.
# ==============================================================================
"""Tests for summary image op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.python.platform
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
from tensorflow.python.ops import image_ops
class SummaryImageOpTest(tf.test.TestCase):
def _AsSummary(self, s):
summ = tf.Summary()
summ.ParseFromString(s)
return summ
def _CheckProto(self, image_summ, shape):
"""Verify that the non-image parts of the image_summ proto match shape."""
# Only the first 3 images are returned.
for v in image_summ.value:
v.image.ClearField("encoded_image_string")
expected = '\n'.join("""
value {
tag: "img/image/%d"
image { height: %d width: %d colorspace: %d }
}""" % ((i,) + shape[1:]) for i in xrange(3))
self.assertProtoEquals(expected, image_summ)
def testImageSummary(self):
np.random.seed(7)
with self.test_session() as sess:
for depth in 1, 3, 4:
shape = (4, 5, 7) + (depth,)
bad_color = [255, 0, 0, 255][:depth]
for positive in False, True:
# Build a mostly random image with one nan
const = np.random.randn(*shape).astype(np.float32)
const[0, 1, 2] = 0 # Make the nan entry not the max
if positive:
const = 1 + np.maximum(const, 0)
scale = 255 / const.reshape(4, -1).max(axis=1)
offset = 0
else:
scale = 127 / np.abs(const.reshape(4, -1)).max(axis=1)
offset = 128
adjusted = np.floor(scale[:, None, None, None] * const + offset)
const[0, 1, 2, depth // 2] = np.nan
# Summarize
summ = tf.image_summary("img", const)
value = sess.run(summ)
self.assertEqual([], summ.get_shape())
image_summ = self._AsSummary(value)
# Decode the first image and check consistency
image = image_ops.decode_png(
image_summ.value[0].image.encoded_image_string).eval()
self.assertAllEqual(image[1, 2], bad_color)
image[1, 2] = adjusted[0, 1, 2]
self.assertAllClose(image, adjusted[0])
# Check the rest of the proto
self._CheckProto(image_summ, shape)
def testImageSummaryUint8(self):
np.random.seed(7)
with self.test_session() as sess:
for depth in 1, 3, 4:
shape = (4, 5, 7) + (depth,)
# Build a random uint8 image
images = np.random.randint(256, size=shape).astype(np.uint8)
tf_images = tf.convert_to_tensor(images)
self.assertEqual(tf_images.dtype, tf.uint8)
# Summarize
summ = tf.image_summary("img", tf_images)
value = sess.run(summ)
self.assertEqual([], summ.get_shape())
image_summ = self._AsSummary(value)
# Decode the first image and check consistency.
# Since we're uint8, everything should be exact.
image = image_ops.decode_png(
image_summ.value[0].image.encoded_image_string).eval()
self.assertAllEqual(image, images[0])
# Check the rest of the proto
self._CheckProto(image_summ, shape)
if __name__ == "__main__":
tf.test.main()
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