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# Copyright 2016 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.
# ==============================================================================
"""Tests for image_ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.image.python.ops import image_ops
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import googletest
_DTYPES = set(
[dtypes.uint8, dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64])
class ImageOpsTest(test_util.TensorFlowTestCase):
def test_zeros(self):
with self.test_session():
for dtype in _DTYPES:
for shape in [(5, 5), (24, 24), (2, 24, 24, 3)]:
for angle in [0, 1, np.pi / 2.0]:
image = array_ops.zeros(shape, dtype)
self.assertAllEqual(
image_ops.rotate(image, angle).eval(),
np.zeros(shape, dtype.as_numpy_dtype()))
def test_rotate_even(self):
with self.test_session():
for dtype in _DTYPES:
image = array_ops.reshape(
math_ops.cast(math_ops.range(36), dtype), (6, 6))
image_rep = array_ops.tile(image[None, :, :, None], [3, 1, 1, 1])
angles = constant_op.constant([0.0, np.pi / 4.0, np.pi / 2.0],
dtypes.float32)
image_rotated = image_ops.rotate(image_rep, angles)
self.assertAllEqual(image_rotated[:, :, :, 0].eval(),
[[[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11],
[12, 13, 14, 15, 16, 17],
[18, 19, 20, 21, 22, 23],
[24, 25, 26, 27, 28, 29],
[30, 31, 32, 33, 34, 35]],
[[0, 3, 4, 11, 17, 0], [2, 3, 9, 16, 23, 23],
[1, 8, 15, 21, 22, 29], [6, 13, 20, 21, 27, 34],
[12, 18, 19, 26, 33, 33], [0, 18, 24, 31, 32, 0]],
[[5, 11, 17, 23, 29, 35], [4, 10, 16, 22, 28, 34],
[3, 9, 15, 21, 27, 33], [2, 8, 14, 20, 26, 32],
[1, 7, 13, 19, 25, 31], [0, 6, 12, 18, 24, 30]]])
def test_rotate_odd(self):
with self.test_session():
for dtype in _DTYPES:
image = array_ops.reshape(
math_ops.cast(math_ops.range(25), dtype), (5, 5))
image_rep = array_ops.tile(image[None, :, :, None], [3, 1, 1, 1])
angles = constant_op.constant([np.pi / 4.0, 1.0, -np.pi / 2.0],
dtypes.float32)
image_rotated = image_ops.rotate(image_rep, angles)
self.assertAllEqual(image_rotated[:, :, :, 0].eval(),
[[[0, 3, 8, 9, 0], [1, 7, 8, 13, 19],
[6, 6, 12, 18, 18], [5, 11, 16, 17, 23],
[0, 15, 16, 21, 0]],
[[0, 3, 9, 14, 0], [2, 7, 8, 13, 19],
[1, 6, 12, 18, 23], [5, 11, 16, 17, 22],
[0, 10, 15, 21, 0]],
[[20, 15, 10, 5, 0], [21, 16, 11, 6, 1],
[22, 17, 12, 7, 2], [23, 18, 13, 8, 3],
[24, 19, 14, 9, 4]]])
def test_compose(self):
with self.test_session():
for dtype in _DTYPES:
image = constant_op.constant(
[[1, 1, 1, 0],
[1, 0, 0, 0],
[1, 1, 1, 0],
[0, 0, 0, 0]], dtype=dtype)
# Rotate counter-clockwise by pi / 2.
rotation = image_ops.angles_to_projective_transforms(np.pi / 2, 4, 4)
# Translate right by 1 (the transformation matrix is always inverted,
# hence the -1).
translation = constant_op.constant([1, 0, -1,
0, 1, 0,
0, 0],
dtype=dtypes.float32)
composed = image_ops.compose_transforms(rotation, translation)
image_transformed = image_ops.transform(image, composed)
self.assertAllEqual(image_transformed.eval(),
[[0, 0, 0, 0],
[0, 1, 0, 1],
[0, 1, 0, 1],
[0, 1, 1, 1]])
def _test_grad(self, shape_to_test):
with self.test_session():
test_image_shape = shape_to_test
test_image = np.random.randn(*test_image_shape)
test_image_tensor = constant_op.constant(
test_image, shape=test_image_shape)
test_transform = image_ops.angles_to_projective_transforms(
np.pi / 2, 4, 4)
output_shape = test_image_shape
output = image_ops.transform(test_image_tensor, test_transform)
left_err = gradient_checker.compute_gradient_error(
test_image_tensor,
test_image_shape,
output,
output_shape,
x_init_value=test_image)
self.assertLess(left_err, 1e-10)
def test_grad(self):
self._test_grad([16, 16])
self._test_grad([4, 12, 12])
self._test_grad([3, 4, 12, 12])
if __name__ == "__main__":
googletest.main()
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