def _perspective_correct_attributes(attribute, barycentrics, triangles,
triangle_index, len_batch_shape):
attribute = tf.gather(attribute, triangles, axis=-2)
attribute_per_pixel = tf.gather(
attribute, triangle_index, axis=-3, batch_dims=len_batch_shape)
return glm.interpolate_attributes(attribute_per_pixel, barycentrics)
def test_interpolate_attributes_random(self):
"""Checks the output of interpolate_attributes."""
attributes = np.random.uniform(-1.0, 1.0, size=(3,))
barycentric = np.random.uniform(0.0, 1.0, size=(3,))
barycentric = barycentric / np.linalg.norm(
barycentric, axis=-1, ord=1, keepdims=True)
groundtruth = np.sum(attributes * barycentric, keepdims=True)
attributes = np.reshape(attributes, (3, 1))
prediction = glm.interpolate_attributes(attributes, barycentric)
self.assertAllClose(groundtruth, prediction)