def set_default_parameters():
""" Loads and sets default parameters defined in DefaultConfig.py """
# Set default intrinsics
CameraUtility.set_intrinsics_from_blender_params(
DefaultConfig.fov, DefaultConfig.resolution_x,
DefaultConfig.resolution_y, DefaultConfig.clip_start,
DefaultConfig.clip_end, DefaultConfig.pixel_aspect_x,
DefaultConfig.pixel_aspect_y, DefaultConfig.shift_x,
DefaultConfig.shift_y, "FOV")
CameraUtility.set_stereo_parameters(
DefaultConfig.stereo_convergence_mode,
DefaultConfig.stereo_convergence_distance,
DefaultConfig.stereo_interocular_distance)
# Init renderer
RendererUtility.init()
RendererUtility.set_samples(DefaultConfig.samples)
addon_utils.enable("render_auto_tile_size")
RendererUtility.toggle_auto_tile_size(True)
# Set number of cpu cores used for rendering (1 thread is always used for coordination => 1
# cpu thread means GPU-only rendering)
RendererUtility.set_cpu_threads(1)
RendererUtility.set_denoiser(DefaultConfig.denoiser)
RendererUtility.set_simplify_subdivision_render(
DefaultConfig.simplify_subdivision_render)
RendererUtility.set_light_bounces(
DefaultConfig.diffuse_bounces, DefaultConfig.glossy_bounces,
DefaultConfig.ao_bounces_render, DefaultConfig.max_bounces,
DefaultConfig.transmission_bounces,
DefaultConfig.transparency_bounces, DefaultConfig.volume_bounces)
def _configure_renderer(self,
default_samples=256,
use_denoiser=False,
default_denoiser="Intel"):
"""
Sets many different render parameters which can be adjusted via the config.
:param default_samples: Default number of samples to render for each pixel
:param use_denoiser: If true, a denoiser is used, only use this on color information
:param default_denoiser: Either "Intel" or "Blender", "Intel" performs much better in most cases
"""
RendererUtility.init()
RendererUtility.set_samples(
self.config.get_int("samples", default_samples))
if self.config.has_param("use_adaptive_sampling"):
RendererUtility.set_adaptive_sampling(
self.config.get_float("use_adaptive_sampling"))
if self.config.get_bool("auto_tile_size", True):
RendererUtility.toggle_auto_tile_size(True)
else:
RendererUtility.toggle_auto_tile_size(False)
RendererUtility.set_tile_size(self.config.get_int("tile_x"),
self.config.get_int("tile_y"))
# Set number of cpu cores used for rendering (1 thread is always used for coordination => 1
# cpu thread means GPU-only rendering)
RendererUtility.set_cpu_threads(self.config.get_int("cpu_threads", 1))
print('Resolution: {}, {}'.format(
bpy.context.scene.render.resolution_x,
bpy.context.scene.render.resolution_y))
RendererUtility.set_denoiser(None if not use_denoiser else self.config.
get_string("denoiser", default_denoiser))
RendererUtility.set_simplify_subdivision_render(
self.config.get_int("simplify_subdivision_render", 3))
RendererUtility.set_light_bounces(
self.config.get_int("diffuse_bounces", 3),
self.config.get_int("glossy_bounces", 0),
self.config.get_int("ao_bounces_render", 3),
self.config.get_int("max_bounces", 3),
self.config.get_int("transmission_bounces", 0),
self.config.get_int("transparency_bounces", 8),
self.config.get_int("volume_bounces", 0))
RendererUtility.toggle_stereo(self.config.get_bool("stereo", False))
self._use_alpha_channel = self.config.get_bool('use_alpha', False)
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
CameraUtility.add_camera_pose(cam2world_matrix)
poses += 1
tries += 1
# activate distance rendering
RendererUtility.enable_distance_output()
# set the amount of samples, which should be used for the color rendering
RendererUtility.set_samples(350)
RendererUtility.set_light_bounces(max_bounces=200,
diffuse_bounces=200,
glossy_bounces=200,
transmission_bounces=200,
transparent_max_bounces=200)
# render the whole pipeline
data = RendererUtility.render()
# post process the data and remove the redundant channels in the distance image
data["depth"] = PostProcessingUtility.dist2depth(data["distance"])
del data["distance"]
# write the data to a .hdf5 container
WriterUtility.save_to_hdf5(args.output_dir, data)
def render(output_dir,
temp_dir,
get_forward_flow,
get_backward_flow,
blender_image_coordinate_style=False,
forward_flow_output_file_prefix="forward_flow_",
forward_flow_output_key="forward_flow",
backward_flow_output_file_prefix="backward_flow_",
backward_flow_output_key="backward_flow"):
""" Renders the optical flow (forward and backward) for all frames.
:param output_dir: The directory to write images to.
:param temp_dir: The directory to write intermediate data to.
:param get_forward_flow: Whether to render forward optical flow.
:param get_backward_flow: Whether to render backward optical flow.
:param blender_image_coordinate_style: Whether to specify the image coordinate system at the bottom left (blender default; True) or top left (standard convention; False).
:param forward_flow_output_file_prefix: The file prefix that should be used when writing forward flow to a file.
:param forward_flow_output_key: The key which should be used for storing forward optical flow values.
:param backward_flow_output_file_prefix: The file prefix that should be used when writing backward flow to a file.
:param backward_flow_output_key: The key which should be used for storing backward optical flow values.
"""
if get_forward_flow is False and get_backward_flow is False:
raise Exception(
"Take the FlowRenderer Module out of the config if both forward and backward flow are set to False!"
)
with Utility.UndoAfterExecution():
RendererUtility.init()
RendererUtility.set_samples(1)
RendererUtility.set_adaptive_sampling(0)
RendererUtility.set_denoiser(None)
RendererUtility.set_light_bounces(1, 0, 0, 1, 0, 8, 0)
FlowRendererUtility._output_vector_field(get_forward_flow,
get_backward_flow,
output_dir)
# only need to render once; both fwd and bwd flow will be saved
temporary_fwd_flow_file_path = os.path.join(temp_dir, 'fwd_flow_')
temporary_bwd_flow_file_path = os.path.join(temp_dir, 'bwd_flow_')
RendererUtility.render(temp_dir, "bwd_flow_", None)
# After rendering: convert to optical flow or calculate hsv visualization, if desired
for frame in range(bpy.context.scene.frame_start,
bpy.context.scene.frame_end):
# temporarily save respective vector fields
if get_forward_flow:
file_path = temporary_fwd_flow_file_path + "%04d" % frame + ".exr"
fwd_flow_field = load_image(
file_path, num_channels=4).astype(np.float32)
if not blender_image_coordinate_style:
fwd_flow_field[:, :, 1] = fwd_flow_field[:, :, 1] * -1
fname = os.path.join(
output_dir,
forward_flow_output_file_prefix) + '%04d' % frame
forward_flow = fwd_flow_field * -1 # invert forward flow to point at next frame
np.save(fname + '.npy', forward_flow[:, :, :2])
if get_backward_flow:
file_path = temporary_bwd_flow_file_path + "%04d" % frame + ".exr"
bwd_flow_field = load_image(
file_path, num_channels=4).astype(np.float32)
if not blender_image_coordinate_style:
bwd_flow_field[:, :, 1] = bwd_flow_field[:, :, 1] * -1
fname = os.path.join(
output_dir,
backward_flow_output_file_prefix) + '%04d' % frame
np.save(fname + '.npy', bwd_flow_field[:, :, :2])
# register desired outputs
if get_forward_flow:
Utility.register_output(output_dir,
forward_flow_output_file_prefix,
forward_flow_output_key, '.npy', '2.0.0')
if get_backward_flow:
Utility.register_output(output_dir,
backward_flow_output_file_prefix,
backward_flow_output_key, '.npy', '2.0.0')
def render(output_dir,
temp_dir,
used_attributes,
used_default_values={},
file_prefix="segmap_",
output_key="segmap",
segcolormap_output_file_prefix="class_inst_col_map",
segcolormap_output_key="segcolormap",
use_alpha_channel=False,
render_colorspace_size_per_dimension=2048):
""" Renders segmentation maps for all frames.
:param output_dir: The directory to write images to.
:param temp_dir: The directory to write intermediate data to.
:param used_attributes: The attributes to be used for color mapping.
:param used_default_values: The default values used for the keys used in used_attributes.
:param file_prefix: The prefix to use for writing the images.
:param output_key: The key to use for registering the output.
:param segcolormap_output_file_prefix: The prefix to use for writing the segmation-color map csv.
:param segcolormap_output_key: The key to use for registering the segmation-color map output.
:param use_alpha_channel: If true, the alpha channel stored in .png textures is used.
:param render_colorspace_size_per_dimension: As we use float16 for storing the rendering, the interval of integers which can be precisely stored is [-2048, 2048]. As blender does not allow negative values for colors, we use [0, 2048] ** 3 as our color space which allows ~8 billion different colors/objects. This should be enough.
"""
with Utility.UndoAfterExecution():
RendererUtility.init()
RendererUtility.set_samples(1)
RendererUtility.set_adaptive_sampling(0)
RendererUtility.set_denoiser(None)
RendererUtility.set_light_bounces(1, 0, 0, 1, 0, 8, 0)
# Get objects with meshes (i.e. not lights or cameras)
objs_with_mats = get_all_blender_mesh_objects()
colors, num_splits_per_dimension, used_objects = SegMapRendererUtility._colorize_objects_for_instance_segmentation(
objs_with_mats, use_alpha_channel,
render_colorspace_size_per_dimension)
bpy.context.scene.cycles.filter_width = 0.0
if use_alpha_channel:
MaterialLoaderUtility.add_alpha_channel_to_textures(
blurry_edges=False)
# Determine path for temporary and for final output
temporary_segmentation_file_path = os.path.join(temp_dir, "seg_")
final_segmentation_file_path = os.path.join(
output_dir, file_prefix)
RendererUtility.set_output_format("OPEN_EXR", 16)
RendererUtility.render(temp_dir, "seg_", None)
# Find optimal dtype of output based on max index
for dtype in [np.uint8, np.uint16, np.uint32]:
optimal_dtype = dtype
if np.iinfo(optimal_dtype).max >= len(colors) - 1:
break
if 'class' in used_default_values:
used_default_values['cp_category_id'] = used_default_values[
'class']
if isinstance(used_attributes, str):
# only one result is requested
result_channels = 1
used_attributes = [used_attributes]
elif isinstance(used_attributes, list):
result_channels = len(used_attributes)
else:
raise Exception(
"The type of this is not supported here: {}".format(
used_attributes))
save_in_csv_attributes = {}
# define them for the avoid rendering case
there_was_an_instance_rendering = False
list_of_used_attributes = []
# Check if stereo is enabled
if bpy.context.scene.render.use_multiview:
suffixes = ["_L", "_R"]
else:
suffixes = [""]
# After rendering
for frame in range(
bpy.context.scene.frame_start,
bpy.context.scene.frame_end): # for each rendered frame
for suffix in suffixes:
file_path = temporary_segmentation_file_path + (
"%04d" % frame) + suffix + ".exr"
segmentation = load_image(file_path)
print(file_path, segmentation.shape)
segmap = Utility.map_back_from_equally_spaced_equidistant_values(
segmentation, num_splits_per_dimension,
render_colorspace_size_per_dimension)
segmap = segmap.astype(optimal_dtype)
used_object_ids = np.unique(segmap)
max_id = np.max(used_object_ids)
if max_id >= len(used_objects):
raise Exception(
"There are more object colors than there are objects"
)
combined_result_map = []
there_was_an_instance_rendering = False
list_of_used_attributes = []
used_channels = []
for channel_id in range(result_channels):
resulting_map = np.empty(
(segmap.shape[0], segmap.shape[1]))
was_used = False
current_attribute = used_attributes[channel_id]
org_attribute = current_attribute
# if the class is used the category_id attribute is evaluated
if current_attribute == "class":
current_attribute = "cp_category_id"
# in the instance case the resulting ids are directly used
if current_attribute == "instance":
there_was_an_instance_rendering = True
resulting_map = segmap
was_used = True
# a non default value was also used
non_default_value_was_used = True
else:
if current_attribute != "cp_category_id":
list_of_used_attributes.append(
current_attribute)
# for the current attribute remove cp_ and _csv, if present
used_attribute = current_attribute
if used_attribute.startswith("cp_"):
used_attribute = used_attribute[len("cp_"):]
# check if a default value was specified
default_value_set = False
if current_attribute in used_default_values or used_attribute in used_default_values:
default_value_set = True
if current_attribute in used_default_values:
default_value = used_default_values[
current_attribute]
elif used_attribute in used_default_values:
default_value = used_default_values[
used_attribute]
last_state_save_in_csv = None
# this avoids that for certain attributes only the default value is written
non_default_value_was_used = False
# iterate over all object ids
for object_id in used_object_ids:
is_default_value = False
# get the corresponding object via the id
current_obj = used_objects[object_id]
# if the current obj has a attribute with that name -> get it
if hasattr(current_obj, used_attribute):
used_value = getattr(
current_obj, used_attribute)
# if the current object has a custom property with that name -> get it
elif current_attribute.startswith(
"cp_"
) and used_attribute in current_obj:
used_value = current_obj[used_attribute]
elif current_attribute.startswith("cf_"):
if current_attribute == "cf_basename":
used_value = current_obj.name
if "." in used_value:
used_value = used_value[:used_value
.rfind("."
)]
elif default_value_set:
# if none of the above applies use the default value
used_value = default_value
is_default_value = True
else:
# if the requested current_attribute is not a custom property or a attribute
# or there is a default value stored
# it throws an exception
raise Exception(
"The obj: {} does not have the "
"attribute: {}, striped: {}. Maybe try a default "
"value.".format(
current_obj.name,
current_attribute, used_attribute))
# check if the value should be saved as an image or in the csv file
save_in_csv = False
try:
resulting_map[segmap ==
object_id] = used_value
was_used = True
if not is_default_value:
non_default_value_was_used = True
# save everything which is not instance also in the .csv
if current_attribute != "instance":
save_in_csv = True
except ValueError:
save_in_csv = True
if last_state_save_in_csv is not None and last_state_save_in_csv != save_in_csv:
raise Exception(
"During creating the mapping, the saving to an image or a csv file "
"switched, this might indicated that the used default value, does "
"not have the same type as the returned value, "
"for: {}".format(current_attribute))
last_state_save_in_csv = save_in_csv
if save_in_csv:
if object_id in save_in_csv_attributes:
save_in_csv_attributes[object_id][
used_attribute] = used_value
else:
save_in_csv_attributes[object_id] = {
used_attribute: used_value
}
if was_used and non_default_value_was_used:
used_channels.append(org_attribute)
combined_result_map.append(resulting_map)
fname = final_segmentation_file_path + ("%04d" %
frame) + suffix
# combine all resulting images to one image
resulting_map = np.stack(combined_result_map, axis=2)
# remove the unneeded third dimension
if resulting_map.shape[2] == 1:
resulting_map = resulting_map[:, :, 0]
np.save(fname, resulting_map)
if not there_was_an_instance_rendering:
if len(list_of_used_attributes) > 0:
raise Exception(
"There were attributes specified in the may_by, which could not be saved as "
"there was no \"instance\" may_by key used. This is true for this/these "
"keys: {}".format(", ".join(list_of_used_attributes)))
# if there was no instance rendering no .csv file is generated!
# delete all saved infos about .csv
save_in_csv_attributes = {}
# write color mappings to file
if save_in_csv_attributes:
csv_file_path = os.path.join(
output_dir, segcolormap_output_file_prefix + ".csv")
with open(csv_file_path, 'w', newline='') as csvfile:
# get from the first element the used field names
fieldnames = ["idx"]
# get all used object element keys
for object_element in save_in_csv_attributes.values():
fieldnames.extend(list(object_element.keys()))
break
for channel_name in used_channels:
fieldnames.append("channel_{}".format(channel_name))
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
# save for each object all values in one row
for obj_idx, object_element in save_in_csv_attributes.items(
):
object_element["idx"] = obj_idx
for i, channel_name in enumerate(used_channels):
object_element["channel_{}".format(
channel_name)] = i
writer.writerow(object_element)
Utility.register_output(output_dir, file_prefix, output_key, ".npy",
"2.0.0")
if save_in_csv_attributes:
Utility.register_output(output_dir,
segcolormap_output_file_prefix,
segcolormap_output_key,
".csv",
"2.0.0",
unique_for_camposes=False)