def test_distortion_factor_preset_zero_distortion_coefficient(self):
"""Tests distortion_factor at zero distortion coefficient."""
squared_radii = _get_random_radii() * 2.0
distortion, mask = quadratic_radial_distortion.distortion_factor(
squared_radii, 0.0)
with self.subTest(name='distortion'):
self.assertAllClose(tf.ones_like(squared_radii), distortion)
# No overflow when distortion_coefficient = 0.0.
with self.subTest(name='mask'):
self.assertAllInSet(mask, (False, ))
def test_distortion_factor_preset_zero_radius(self):
"""Tests distortion_factor at the corner case of zero radius."""
squared_radii = _get_zeros_radii()
distortion_coefficient = _get_random_coefficient() - 0.5
distortion, mask = quadratic_radial_distortion.distortion_factor(
squared_radii, distortion_coefficient)
with self.subTest(name='distortion'):
self.assertAllClose(np.ones_like(squared_radii), distortion)
with self.subTest(name='mask'):
self.assertAllInSet(mask, (False, ))
def test_distortion_factor_random_positive_distortion_coefficient(self):
"""Tests that distortion_factor produces the expected outputs."""
squared_radii = _get_random_radii() * 2.0
distortion_coefficient = _get_random_coefficient() * 2.0
distortion, mask = quadratic_radial_distortion.distortion_factor(
squared_radii, distortion_coefficient)
distortion_coefficient = _make_shape_compatible(distortion_coefficient)
with self.subTest(name='distortion'):
self.assertAllClose(1.0 + distortion_coefficient * squared_radii,
distortion)
# No overflow when distortion_coefficient >= 0.0.
with self.subTest(name='mask'):
self.assertAllInSet(mask, (False, ))
def test_distortion_factor_random_negative_distortion_coefficient(self):
"""Tests that distortion_factor produces the expected outputs."""
squared_radii = _get_random_radii() * 2.0
distortion_coefficient = _get_random_coefficient() * -0.2
distortion, mask = quadratic_radial_distortion.distortion_factor(
squared_radii, distortion_coefficient)
distortion_coefficient = _make_shape_compatible(distortion_coefficient)
max_squared_radii = -1.0 / 3.0 / distortion_coefficient
expected_overflow_mask = squared_radii > max_squared_radii
valid_mask = np.logical_not(expected_overflow_mask)
# We assert correctness of the mask, and of all the pixels that are not in
# overflow.
actual_distortion_when_valid = self.evaluate(distortion)[valid_mask]
expected_distortion_when_valid = (
1.0 + distortion_coefficient * squared_radii)[valid_mask]
with self.subTest(name='distortion'):
self.assertAllClose(expected_distortion_when_valid,
actual_distortion_when_valid)
with self.subTest(name='mask'):
self.assertAllEqual(expected_overflow_mask, mask)
