for i in range(15):
interior_objects.extend(
IKEALoader.load(args.ikea_path, ["bed", "chair", "desk", "bookshelf"]))
# Construct random room and fill with interior_objects
objects = RandomRoomConstructor.construct(25,
interior_objects,
materials,
amount_of_extrusions=5)
# Bring light into the room
SurfaceLighting.run([obj for obj in objects if obj.get_name() == "Ceiling"],
emission_strength=4.0)
# Init bvh tree containing all mesh objects
bvh_tree = MeshObject.create_bvh_tree_multi_objects(objects)
floor = [obj for obj in objects if obj.get_name() == "Floor"][0]
poses = 0
tries = 0
while tries < 10000 and poses < 5:
# Sample point
location = UpperRegionSampler.sample(floor, min_height=1.5, max_height=1.8)
# Sample rotation
rotation = np.random.uniform([1.0, 0, 0], [1.4217, 0, 6.283185307])
cam2world_matrix = MathUtility.build_transformation_mat(location, rotation)
# Check that obstacles are at least 1 meter away from the camera and make sure the view interesting enough
if CameraValidation.perform_obstacle_in_view_check(cam2world_matrix, {"min": 1.2}, bvh_tree) and \
CameraValidation.scene_coverage_score(cam2world_matrix) > 0.4 and \
floor.position_is_above_object(location):
# Persist camera pose
Initializer.init()
# load the objects into the scene
label_mapping = LabelIdMapping.from_csv(
Utility.resolve_path(
os.path.join('resources', 'id_mappings', 'nyu_idset.csv')))
objs = SuncgLoader.load(args.house, label_mapping)
# makes Suncg objects emit light
SuncgLighting.light()
# Init sampler for sampling locations inside the loaded suncg house
point_sampler = SuncgPointInRoomSampler(objs)
# Init bvh tree containing all mesh objects
bvh_tree = MeshObject.create_bvh_tree_multi_objects(
[o for o in objs if isinstance(o, MeshObject)])
poses = 0
tries = 0
while tries < 10000 and poses < 5:
# Sample point inside house
height = np.random.uniform(0.5, 2)
location, _ = point_sampler.sample(height)
# Sample rotation (fix around X and Y axis)
euler_rotation = np.random.uniform([1.2217, 0, 0],
[1.2217, 0, 6.283185307])
cam2world_matrix = MathUtility.build_transformation_mat(
location, euler_rotation)
# Check that obstacles are at least 1 meter away from the camera and make sure the view interesting enough
if CameraValidation.perform_obstacle_in_view_check(
def _sample_cam_poses(self, config):
""" Samples camera poses according to the given config
:param config: The config object
"""
cam_ob = bpy.context.scene.camera
cam = cam_ob.data
# Set global parameters
self.sqrt_number_of_rays = config.get_int("sqrt_number_of_rays", 10)
self.max_tries = config.get_int("max_tries", 10000)
self.proximity_checks = config.get_raw_dict("proximity_checks", {})
self.excluded_objects_in_proximity_check = config.get_list(
"excluded_objs_in_proximity_check", [])
self.min_interest_score = config.get_float("min_interest_score", 0.0)
self.interest_score_range = config.get_float("interest_score_range",
self.min_interest_score)
self.interest_score_step = config.get_float("interest_score_step", 0.1)
self.special_objects = config.get_list("special_objects", [])
self.special_objects_weight = config.get_float(
"special_objects_weight", 2)
self._above_objects = MeshObject.convert_to_meshes(
config.get_list("check_if_pose_above_object_list", []))
self.check_visible_objects = MeshObject.convert_to_meshes(
config.get_list("check_if_objects_visible", []))
# Set camera intrinsics
self._set_cam_intrinsics(
cam, Config(self.config.get_raw_dict("intrinsics", {})))
if self.proximity_checks:
# needs to build an bvh tree
mesh_objects = [
MeshObject(obj) for obj in get_all_blender_mesh_objects()
if obj not in self.excluded_objects_in_proximity_check
]
self.bvh_tree = MeshObject.create_bvh_tree_multi_objects(
mesh_objects)
if self.interest_score_step <= 0.0:
raise Exception(
"Must have an interest score step size bigger than 0")
# Determine the number of camera poses to sample
number_of_poses = config.get_int("number_of_samples", 1)
print("Sampling " + str(number_of_poses) + " cam poses")
# Start with max interest score
self.interest_score = self.interest_score_range
# Init
all_tries = 0
tries = 0
existing_poses = []
for i in range(number_of_poses):
# Do until a valid pose has been found or the max number of tries has been reached
while tries < self.max_tries:
tries += 1
all_tries += 1
# Sample a new cam pose and check if its valid
if self.sample_and_validate_cam_pose(config, existing_poses):
break
# If max tries has been reached
if tries >= self.max_tries:
# Decrease interest score and try again, if we have not yet reached minimum
continue_trying, self.interest_score = CameraValidation.decrease_interest_score(
self.interest_score, self.min_interest_score,
self.interest_score_step)
if continue_trying:
tries = 0
print(str(all_tries) + " tries were necessary")