def testComplexShading(self):
"""Tests specular highlights, colors, and multiple lights per image."""
# rotate the cube for the test:
model_transforms = camera_utils.euler_matrices(
[[-20.0, 0.0, 60.0], [45.0, 60.0, 0.0]])[:, :3, :3]
vertices_world_space = tf.matmul(
tf.stack([self.cube_vertices, self.cube_vertices]),
model_transforms,
transpose_b=True)
normals_world_space = tf.matmul(
tf.stack([self.cube_normals, self.cube_normals]),
model_transforms,
transpose_b=True)
# camera position:
eye = tf.constant([[0.0, 0.0, 6.0], [0., 0.2, 18.0]], dtype=tf.float32)
center = tf.constant([[0.0, 0.0, 0.0], [0.1, -0.1, 0.1]], dtype=tf.float32)
world_up = tf.constant(
[[0.0, 1.0, 0.0], [0.1, 1.0, 0.15]], dtype=tf.float32)
fov_y = tf.constant([40., 13.3], dtype=tf.float32)
near_clip = tf.constant(0.1, dtype=tf.float32)
far_clip = tf.constant(25.0, dtype=tf.float32)
image_width = 640
image_height = 480
light_positions = tf.constant([[[0.0, 0.0, 6.0], [1.0, 2.0, 6.0]],
[[0.0, -2.0, 4.0], [1.0, 3.0, 4.0]]])
light_intensities = tf.constant(
[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], [[2.0, 0.0, 1.0], [0.0, 2.0,
1.0]]],
dtype=tf.float32)
# pyformat: disable
vertex_diffuse_colors = tf.constant(2*[[[1.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0],
[1.0, 1.0, 1.0],
[1.0, 1.0, 0.0],
[1.0, 0.0, 1.0],
[0.0, 1.0, 1.0],
[0.5, 0.5, 0.5]]],
dtype=tf.float32)
vertex_specular_colors = tf.constant(2*[[[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0],
[1.0, 1.0, 1.0],
[1.0, 1.0, 0.0],
[1.0, 0.0, 1.0],
[0.0, 1.0, 1.0],
[0.5, 0.5, 0.5],
[1.0, 0.0, 0.0]]],
dtype=tf.float32)
# pyformat: enable
shininess_coefficients = 6.0 * tf.ones([2, 8], dtype=tf.float32)
ambient_color = tf.constant(
[[0., 0., 0.], [0.1, 0.1, 0.2]], dtype=tf.float32)
renders = mesh_renderer.mesh_renderer(
vertices_world_space, self.cube_triangles, normals_world_space,
vertex_diffuse_colors, eye, center, world_up, light_positions,
light_intensities, image_width, image_height, vertex_specular_colors,
shininess_coefficients, ambient_color, fov_y, near_clip, far_clip)
tonemapped_renders = tf.concat(
[
mesh_renderer.tone_mapper(renders[:, :, :, 0:3], 0.7),
renders[:, :, :, 3:4]
],
axis=3)
# Check that shininess coefficient broadcasting works by also rendering
# with a scalar shininess coefficient, and ensuring the result is identical:
broadcasted_renders = mesh_renderer.mesh_renderer(
vertices_world_space, self.cube_triangles, normals_world_space,
vertex_diffuse_colors, eye, center, world_up, light_positions,
light_intensities, image_width, image_height, vertex_specular_colors,
6.0, ambient_color, fov_y, near_clip, far_clip)
tonemapped_broadcasted_renders = tf.concat(
[
mesh_renderer.tone_mapper(broadcasted_renders[:, :, :, 0:3], 0.7),
broadcasted_renders[:, :, :, 3:4]
],
axis=3)
with self.test_session() as sess:
images, broadcasted_images = sess.run(
[tonemapped_renders, tonemapped_broadcasted_renders], feed_dict={})
for image_id in range(images.shape[0]):
target_image_name = 'Colored_Cube_%i.png' % image_id
baseline_image_path = os.path.join(self.test_data_directory,
target_image_name)
test_utils.expect_image_file_and_render_are_near(
self, sess, baseline_image_path, images[image_id, :, :, :])
test_utils.expect_image_file_and_render_are_near(
self, sess, baseline_image_path,
broadcasted_images[image_id, :, :, :])
def testComplexShading(self):
"""Test specular highlights, colors, and multiple lights per image."""
model_transforms = camera_utils.euler_matrices(
[[-20.0, 0.0, 60.0], [45.0, 60.0, 0.0]])[:, :3, :3]
vertices_world_space = torch.matmul(
torch.stack([self.cube_vertices, self.cube_vertices]),
model_transforms.transpose())
normals_world_space = torch.matmul(
torch.stack([self.cube_normals, self.cube_normals]),
model_transforms.transpose())
# camera position:
eye = torch.tensor([[0.0, 0.0, 6.0], [0.0, 0.2, 18.0]], dtype=torch.float32)
center = torch.tensor([[0.0, 0.0, 0.0], [0.1, -0.1, 0.1]], dtype=torch.float32)
world_up = torch.constant([[0.0, 1.0, 0.0], [0.1, 1.0, 0.15]], dtype=torch.float32)
fov_y = torch.tensor([40.0, 13.3], dtype=torch.float32)
near_clip = 0.1
far_clip = 25.0
image_width = 640
image_height = 480
light_positions = torch.tensor([[[0.0, 0.0, 6.0], [1.0, 2.0, 6.0]],
[[0.0, -2.0, 4.0], [1.0, 3.0, 4.0]]])
light_intensities = torch.tensor(
[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
[[2.0, 0.0, 1.0], [0.0, 2.0, 1.0]]],
dtype=torch.float32)
vertex_diffuse_colors = torch.tensor(2*[[[1.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0],
[1.0, 1.0, 1.0],
[1.0, 1.0, 0.0],
[1.0, 0.0, 1.0],
[0.0, 1.0, 1.0],
[0.5, 0.5, 0.5]]],
dtype=torch.float32)
vertex_specular_colors = torch.tensor(2*[[[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0],
[1.0, 1.0, 1.0],
[1.0, 1.0, 0.0],
[1.0, 0.0, 1.0],
[0.0, 1.0, 1.0],
[0.5, 0.5, 0.5],
[1.0, 0.0, 0.0]]],
dtype=torch.float32)
shininess_coefficients = 6.0 * torch.ones([2, 8], dtype=torch.float32)
ambient_color = torch.tensor([[0.0, 0.0, 0.0], [0.1, 0.1, 0.2]], dtype=torch.float32)
renders = mesh_renderer.mesh_renderer(
vertices_world_space,
self.cube_triangles,
normals_world_space,
vertex_diffuse_colors,
eye,
center,
world_up,
light_positions,
light_intensities,
image_width,
image_height,
vertex_specular_colors,
shininess_coefficients,
ambient_color,
fov_y,
near_clip,
far_clip)
tonemapped_renders = torch.cat([
mesh_renderer.tone_mapper(renders[:, :, :, 0:3], 0.7),
renders[:, :, :, 3:4]
],
dim=3)
# Check that shininess coefficient broadcasting works by also rendering
# with a scalar shininess coefficient, and ensuring the result is identical:
broadcasted_renders = mesh_renderer.mesh_renderer(
vertices_world_space,
self.cube_triangles,
normals_world_space,
vertex_diffuse_colors,
eye,
center,
world_up,
light_positions,
light_intensities,
image_width,
image_height,
vertex_specular_colors,
6.0,
ambient_color,
fov_y,
near_clip,
far_clip)
tonemapped_broadcasted_renders = torch.cat([
mesh_renderer.tone_mapper(broadcasted_renders[:, :, :, 0:3], 0.7),
broadcasted_renders[:, :, :, 3:4]
],
dim=3)
for image_id in range(renders.shape[0]):
target_image_name = "Colored_Cube_%i.png" % image_id
baseline_image_path = os.path.join(self.test_data_directory,
target_image_name)
test_utils.expect_image_file_and_render_are_near(
self, baseline_image_path, tonemapped_renders[image_id, :, :, :])
test_utils.expect_image_file_and_render_are_near(
self, baseline_image_path, tonemapped_broadcasted_renders[image_id, :, :, :])