def test_perspective_transform_jacobian_random(self):
"""Tests the Jacobian of the transform function."""
tensor_shape = np.random.randint(2, 4, size=4)
image_init = np.random.uniform(0.0, 1.0, size=tensor_shape.tolist())
transformation_init = np.random.uniform(
0.0, 1.0, size=(tensor_shape[0], 3, 3))
self.assert_jacobian_is_correct_fn(
lambda x: transformer.perspective_transform(x, transformation_init),
[image_init])
self.assert_jacobian_is_correct_fn(
lambda x: transformer.perspective_transform(image_init, x),
[transformation_init])
def test_perspective_transform_integer_centers_preset(self, dtype,
interpolation):
"""Tests that we can reproduce the results of tfa_image.transform."""
image = tf.constant(
((1.0, 2.0, 3.0), (4.0, 5.0, 6.0), (7.0, 8.0, 9.0), (10.0, 11.0, 12.0)),
dtype=dtype)
scale = 3
transformation = tf.constant(
((1.0 / scale, 0.0, 0.0), (0.0, 1.0 / scale, 0.0), (0.0, 0.0, 1.0)),
dtype=dtype)
image_shape = tf.shape(input=image)
image_resized_shape = image_shape * scale
image = image[tf.newaxis, ..., tf.newaxis]
transformation = transformation[tf.newaxis, ...]
image_resized = tfa_image.transform(
tf.cast(image, tf.float32),
tf.cast(
tfa_image.transform_ops.matrices_to_flat_transforms(transformation),
tf.float32),
interpolation=interpolation,
output_shape=image_resized_shape)
image_transformed = transformer.perspective_transform(
image,
transformation,
resampling_type=transformer.ResamplingType.NEAREST
if interpolation == "NEAREST" else transformer.ResamplingType.BILINEAR,
pixel_type=transformer.PixelType.INTEGER,
output_shape=image_resized_shape)
self.assertAllClose(image_resized, image_transformed)
def test_perspective_transform_half_integer_centers_preset(
self, dtype, interpolation):
"""Tests that we can reproduce the results of tf.image.resize."""
image = tf.constant(
((1.0, 2.0, 3.0), (4.0, 5.0, 6.0), (7.0, 8.0, 9.0), (10.0, 11.0, 12.0)),
dtype=dtype)
scale = 3
transformation = tf.constant(
((1.0 / scale, 0.0, 0.0), (0.0, 1.0 / scale, 0.0), (0.0, 0.0, 1.0)),
dtype=dtype)
image_shape = tf.shape(input=image)
image_resized_shape = image_shape * scale
image = image[tf.newaxis, ..., tf.newaxis]
transformation = transformation[tf.newaxis, ...]
image_resized = tf.image.resize(
image,
size=image_resized_shape,
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR
if interpolation == "NEAREST" else tf.image.ResizeMethod.BILINEAR)
image_transformed = transformer.perspective_transform(
image,
transformation,
resampling_type=transformer.ResamplingType.NEAREST
if interpolation == "NEAREST" else transformer.ResamplingType.BILINEAR,
border_type=transformer.BorderType.DUPLICATE,
output_shape=image_resized_shape)
self.assertAllClose(image_resized, image_transformed)