def test_bilinear(self):
image = tf.constant(
# yapf: disable
[[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 0, 1, 0],
[0, 1, 1, 1, 0], [0, 0, 0, 0, 0]],
# yapf: enable
tf.float32)
# The following result matches:
# >>> scipy.ndimage.rotate(image, 45, order=1, reshape=False)
# which uses spline interpolation of order 1, equivalent to bilinear
# interpolation.
self.assertAllClose(
transform_ops.rotate(image, np.pi / 4.0, interpolation="BILINEAR"),
# yapf: disable
[[0.000, 0.000, 0.343, 0.000, 0.000],
[0.000, 0.586, 0.914, 0.586, 0.000],
[0.343, 0.914, 0.000, 0.914, 0.343],
[0.000, 0.586, 0.914, 0.586, 0.000],
[0.000, 0.000, 0.343, 0.000, 0.000]],
# yapf: enable
atol=0.001)
# yapf: disable
self.assertAllClose(
transform_ops.rotate(
image, np.pi / 4.0, interpolation="NEAREST"),
[[0, 0, 1, 0, 0],
[0, 1, 1, 1, 0],
[1, 1, 0, 1, 1],
[0, 1, 1, 1, 0],
[0, 0, 1, 0, 0]])
def test_bilinear_uint8():
image = tf.constant(
np.asarray(
[
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 255, 255, 255, 0.0],
[0.0, 255, 0.0, 255, 0.0],
[0.0, 255, 255, 255, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
],
np.uint8,
),
tf.uint8,
)
# == np.rint((expected image above) * 255)
transformed = transform_ops.rotate(image, np.pi / 4.0, interpolation="BILINEAR")
np.testing.assert_equal(
transformed.numpy(),
[
[0.0, 0.0, 87.0, 0.0, 0.0],
[0.0, 149, 233, 149, 0.0],
[87.0, 233, 0.0, 233, 87.0],
[0.0, 149, 233, 149, 0.0],
[0.0, 0.0, 87.0, 0.0, 0.0],
],
)
def test_rotate_odd(dtype):
image = tf.reshape(tf.cast(tf.range(25), dtype), (5, 5))
image_rep = tf.tile(image[None, :, :, None], [3, 1, 1, 1])
angles = tf.constant([np.pi / 4.0, 1.0, -np.pi / 2.0], tf.float32)
image_rotated = transform_ops.rotate(image_rep, angles)
np.testing.assert_equal(
image_rotated.numpy()[:, :, :, 0],
[
[
[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_rotate_even(dtype):
image = tf.reshape(tf.cast(tf.range(36), dtype), (6, 6))
image_rep = tf.tile(image[None, :, :, None], [3, 1, 1, 1])
angles = tf.constant([0.0, np.pi / 4.0, np.pi / 2.0], tf.float32)
image_rotated = transform_ops.rotate(image_rep, angles)
np.testing.assert_equal(
image_rotated.numpy()[:, :, :, 0],
[
[
[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_even(self):
for dtype in _DTYPES:
image = tf.reshape(tf.cast(tf.range(36), dtype), (6, 6))
image_rep = tf.tile(image[None, :, :, None], [3, 1, 1, 1])
angles = tf.constant([0.0, np.pi / 4.0, np.pi / 2.0], tf.float32)
image_rotated = transform_ops.rotate(image_rep, angles)
# yapf: disable
self.assertAllEqual(
image_rotated[:, :, :, 0],
[[[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_zeros(self):
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 = tf.zeros(shape, dtype)
self.assertAllEqual(transform_ops.rotate(image, angle),
np.zeros(shape, dtype.as_numpy_dtype))
def random_rotation(
img: tf.Tensor,
max_rotation: float = 0.1,
crop: bool = True,
minimum_width: int = 0) -> tf.Tensor: # adapted from SeguinBe
"""
Rotates an image with a random angle.
See https://stackoverflow.com/questions/16702966/rotate-image-and-crop-out-black-borders for formulae
:param img: Tensor
:param max_rotation: maximum angle to rotate (radians)
:param crop: boolean to crop or not the image after rotation
:param minimum_width: minimum width of image after data augmentation
:return:
"""
with tf.name_scope('RandomRotation'):
rotation = tf.random.uniform([],
-max_rotation,
max_rotation,
name='pick_random_angle')
# rotated_image = tf.contrib.image.rotate(img, rotation, interpolation='BILINEAR')
rotated_image = rotate(tf.expand_dims(img, axis=0),
rotation,
interpolation='BILINEAR')
rotated_image = tf.squeeze(rotated_image, axis=0)
if crop:
rotation = tf.abs(rotation)
original_shape = tf.shape(rotated_image)[:2]
h, w = original_shape[0], original_shape[1]
old_l, old_s = tf.cond(h > w, lambda: [h, w], lambda: [w, h])
old_l, old_s = tf.cast(old_l,
tf.float32), tf.cast(old_s, tf.float32)
new_l = (old_l * tf.cos(rotation) -
old_s * tf.sin(rotation)) / tf.cos(2 * rotation)
new_s = (old_s - tf.sin(rotation) * new_l) / tf.cos(rotation)
new_h, new_w = tf.cond(h > w, lambda: [new_l, new_s],
lambda: [new_s, new_l])
new_h, new_w = tf.cast(new_h, tf.int32), tf.cast(new_w, tf.int32)
bb_begin = tf.cast(tf.math.ceil((h - new_h) / 2),
tf.int32), tf.cast(
tf.math.ceil((w - new_w) / 2), tf.int32)
# Test sliced
rotated_image_crop = tf.cond(
tf.logical_and(bb_begin[0] < h - bb_begin[0],
bb_begin[1] < w - bb_begin[1]),
true_fn=lambda: rotated_image[bb_begin[0]:h - bb_begin[0],
bb_begin[1]:w - bb_begin[1], :],
false_fn=lambda: img,
name='check_slices_indices')
# rotated_image_crop = rotated_image[bb_begin[0]:h - bb_begin[0], bb_begin[1]:w - bb_begin[1], :]
# If crop removes the entire image, keep the original image
rotated_image = tf.cond(tf.less_equal(
tf.shape(rotated_image_crop)[1], minimum_width),
true_fn=lambda: img,
false_fn=lambda: rotated_image_crop,
name='check_size_crop')
return rotated_image
def test_zeros(dtype):
for shape in [(5, 5), (24, 24), (2, 24, 24, 3)]:
for angle in [0, 1, np.pi / 2.0]:
image = tf.zeros(shape, dtype)
np.testing.assert_equal(
transform_ops.rotate(image, angle),
np.zeros(shape, dtype.as_numpy_dtype),
)
def test_bilinear():
image = tf.constant(
[
[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 0, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0],
],
tf.float32,
)
# The following result matches:
# >>> scipy.ndimage.rotate(image, 45, order=1, reshape=False)
# which uses spline interpolation of order 1, equivalent to bilinear
# interpolation.
transformed = transform_ops.rotate(image,
np.pi / 4.0,
interpolation="BILINEAR")
np.testing.assert_allclose(
transformed.numpy(),
[
[0.000, 0.000, 0.343, 0.000, 0.000],
[0.000, 0.586, 0.914, 0.586, 0.000],
[0.343, 0.914, 0.000, 0.914, 0.343],
[0.000, 0.586, 0.914, 0.586, 0.000],
[0.000, 0.000, 0.343, 0.000, 0.000],
],
atol=0.001,
)
transformed = transform_ops.rotate(image,
np.pi / 4.0,
interpolation="NEAREST")
np.testing.assert_allclose(
transformed.numpy(),
[
[0, 0, 1, 0, 0],
[0, 1, 1, 1, 0],
[1, 1, 0, 1, 1],
[0, 1, 1, 1, 0],
[0, 0, 1, 0, 0],
],
)
def test_bilinear_uint8(self):
# yapf: disable
image = tf.constant(
np.asarray(
[[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 255, 255, 255, 0.0],
[0.0, 255, 0.0, 255, 0.0],
[0.0, 255, 255, 255, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0]],
np.uint8),
tf.uint8)
# yapf: enable
# == np.rint((expected image above) * 255)
# yapf: disable
self.assertAllEqual(
transform_ops.rotate(image, np.pi / 4.0, interpolation="BILINEAR"),
[[0.0, 0.0, 87., 0.0, 0.0], [0.0, 149, 233, 149, 0.0],
[87., 233, 0.0, 233, 87.], [0.0, 149, 233, 149, 0.0],
[0.0, 0.0, 87., 0.0, 0.0]])
def test_rotate_odd(self):
for dtype in _DTYPES:
image = tf.reshape(tf.cast(tf.range(25), dtype), (5, 5))
image_rep = tf.tile(image[None, :, :, None], [3, 1, 1, 1])
angles = tf.constant([np.pi / 4.0, 1.0, -np.pi / 2.0], tf.float32)
image_rotated = transform_ops.rotate(image_rep, angles)
# yapf: disable
self.assertAllEqual(
image_rotated[:, :, :, 0],
[[[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_rotate_static_shape():
image = tf.linalg.diag([1.0, 2.0, 3.0])
result = transform_ops.rotate(
image, tf.random.uniform((), -1, 1), interpolation="BILINEAR"
)
np.testing.assert_equal(image.get_shape(), result.get_shape())
def test_rotate_static_shape(self):
image = tf.linalg.diag([1., 2., 3.])
result = transform_ops.rotate(image,
tf.random.uniform((), -1, 1),
interpolation="BILINEAR")
self.assertEqual(image.get_shape(), result.get_shape())