def test_render_preset(self):
"""Checks that render returns the expected value."""
x_voxels_init, y_images_init = test_helpers.generate_preset_test_voxels_emission_absorption_render(
)
voxels = tf.convert_to_tensor(value=x_voxels_init)
y_images = tf.convert_to_tensor(value=y_images_init)
y = emission_absorption.render(voxels,
absorption_factor=0.1,
cell_size=0.1)
self.assertAllClose(y_images, y)
def render_voxels_from_blender_camera(voxels,
object_rotation,
object_translation,
height,
width,
focal,
principal_point,
camera_rotation_matrix,
camera_translation_vector,
frustum_size=(256, 256, 512),
absorption_factor=0.1,
cell_size=1.0,
depth_min=0.0,
depth_max=5.0):
"""Renders the voxels according to their position in the world."""
batch_size = voxels.shape[0]
voxel_size = voxels.shape[1]
sampling_volume = sampling_points_from_frustum(height,
width,
focal,
principal_point,
depth_min=depth_min,
depth_max=depth_max,
frustum_size=frustum_size)
sampling_volume = \
place_frustum_sampling_points_at_blender_camera(sampling_volume,
camera_rotation_matrix,
camera_translation_vector)
interpolated_voxels = \
object_rotation_in_blender_world(voxels, object_rotation)
# Adjust the camera (translate the camera instead of the object)
sampling_volume = sampling_volume - object_translation
sampling_volume = sampling_volume/CUBE_BOX_DIM
sampling_volume = sampling_points_to_voxel_index(sampling_volume, voxel_size)
camera_voxels = trilinear.interpolate(interpolated_voxels, sampling_volume)
camera_voxels = tf.reshape(camera_voxels,
[batch_size] + list(frustum_size) + [4])
voxel_image = emission_absorption.render(camera_voxels,
absorption_factor=absorption_factor,
cell_size=cell_size)
return voxel_image