def run(self):
# if the rendering is not performed -> it is probably the debug case.
do_undo = not self._avoid_rendering
with Utility.UndoAfterExecution(perform_undo_op=do_undo):
self._configure_renderer(use_denoiser=True, default_denoiser="Intel")
# In case a previous renderer changed these settings
bpy.context.scene.render.image_settings.color_mode = "RGB"
bpy.context.scene.render.image_settings.file_format = self._image_type
bpy.context.scene.render.image_settings.color_depth = "8"
# only influences jpg quality
bpy.context.scene.render.image_settings.quality = 95
# check if texture less render mode is active
if self._texture_less_mode:
self.change_to_texture_less_render()
if self._use_alpha_channel:
self.add_alpha_channel_to_textures(blurry_edges=True)
self._render("rgb_")
if self._image_type == 'PNG':
self._register_output("rgb_", "colors", ".png", "1.0.0")
elif self._image_type == 'JPEG':
self._register_output("rgb_", "colors", ".jpg", "1.0.0")
else:
raise Exception("Unknown Image Type " + self._image_type)
def run(self):
with Utility.UndoAfterExecution():
self._configure_renderer()
new_mat = self._create_normal_material()
# render normals
bpy.context.scene.cycles.samples = 1 # this gives the best result for emission shader
bpy.context.view_layer.cycles.use_denoising = False
for obj in bpy.context.scene.objects:
if len(obj.material_slots) > 0:
for i in range(len(obj.material_slots)):
if self._use_alpha_channel:
obj.data.materials[
i] = self.add_alpha_texture_node(
obj.material_slots[i].material, new_mat)
else:
obj.data.materials[i] = new_mat
elif hasattr(obj.data, 'materials'):
obj.data.materials.append(new_mat)
# Set the color channel depth of the output to 32bit
bpy.context.scene.render.image_settings.file_format = "OPEN_EXR"
bpy.context.scene.render.image_settings.color_depth = "32"
if self._use_alpha_channel:
self.add_alpha_channel_to_textures(blurry_edges=False)
self._render("normal_")
self._register_output("normal_", "normals", ".exr", "2.0.0")
def run(self):
# if the rendering is not performed -> it is probably the debug case.
do_undo = not self._avoid_output
with Utility.UndoAfterExecution(perform_undo_op=do_undo):
self._configure_renderer(use_denoiser=True,
default_denoiser="Intel")
# check if texture less render mode is active
if self._texture_less_mode:
MaterialLoaderUtility.change_to_texture_less_render(
self._use_alpha_channel)
if self._use_alpha_channel:
MaterialLoaderUtility.add_alpha_channel_to_textures(
blurry_edges=True)
# motion blur
if self._use_motion_blur:
RendererUtility.enable_motion_blur(
self._motion_blur_length,
'TOP' if self._use_rolling_shutter else "NONE",
self._rolling_shutter_length)
self._render("rgb_",
"colors",
enable_transparency=self.config.get_bool(
"transparent_background", False),
file_format=self._image_type)
def run(self):
# get the type of mappings which should be performed
used_attributes = self.config.get_raw_dict("map_by", "class")
used_default_values = self.config.get_raw_dict("default_values", {})
if 'class' in used_default_values:
used_default_values['cp_category_id'] = used_default_values[
'class']
with Utility.UndoAfterExecution():
self._configure_renderer(default_samples=1)
if not self._avoid_output:
SegMapRendererUtility.render(
self._determine_output_dir(),
self._temp_dir,
used_attributes,
used_default_values,
self.config.get_string("output_file_prefix", "segmap_"),
self.config.get_string("output_key", "segmap"),
self.config.get_string("segcolormap_output_file_prefix",
"class_inst_col_map"),
self.config.get_string("segcolormap_output_key",
"segcolormap"),
use_alpha_channel=self._use_alpha_channel,
return_data=False)
def run(self):
# if the rendering is not performed -> it is probably the debug case.
do_undo = not self._avoid_rendering
with Utility.UndoAfterExecution(perform_undo_op=do_undo):
self._configure_renderer(use_denoiser=True,
default_denoiser="Intel")
# In case a previous renderer changed these settings
#Store as RGB by default unless the user specifies store_alpha as true in yaml
bpy.context.scene.render.image_settings.color_mode = "RGBA" if self.config.get_bool(
"transparent_background", False) else "RGB"
#set the background as transparent if transparent_background is true in yaml
bpy.context.scene.render.film_transparent = self.config.get_bool(
"transparent_background", False)
bpy.context.scene.render.image_settings.file_format = self._image_type
bpy.context.scene.render.image_settings.color_depth = "8"
# only influences jpg quality
bpy.context.scene.render.image_settings.quality = 95
# check if texture less render mode is active
if self._texture_less_mode:
self.change_to_texture_less_render()
if self._use_alpha_channel:
self.add_alpha_channel_to_textures(blurry_edges=True)
# motion blur
if self._use_motion_blur:
bpy.context.scene.render.use_motion_blur = True
bpy.context.scene.render.motion_blur_shutter = self._motion_blur_length
# rolling shutter
if self._use_rolling_shutter:
bpy.context.scene.cycles.rolling_shutter_type = 'TOP'
bpy.context.scene.cycles.rolling_shutter_duration = self._rolling_shutter_length
if not self._use_motion_blur:
raise UserWarning(
"Cannot enable rolling shutter because motion blur is not enabled, "
"see setting use_motion_blur in renderer.RgbRenderer module."
)
if self._motion_blur_length <= 0:
raise UserWarning(
"Cannot enable rolling shutter because no motion blur length is specified, "
"see setting motion_blur_length in renderer.RgbRenderer module."
)
self._render("rgb_")
if self._image_type == 'PNG':
self._register_output("rgb_", "colors", ".png", "1.0.0")
elif self._image_type == 'JPEG':
self._register_output("rgb_", "colors", ".jpg", "1.0.0")
else:
raise Exception("Unknown Image Type " + self._image_type)
def run(self):
with Utility.UndoAfterExecution():
self._configure_renderer()
# In case a previous renderer changed these settings
bpy.context.scene.render.image_settings.color_mode = "RGB"
bpy.context.scene.render.image_settings.file_format = "PNG"
bpy.context.scene.render.image_settings.color_depth = "8"
self._render("rgb_")
self._register_output("rgb_", "colors", ".png", "1.0.0")
def run(self):
with Utility.UndoAfterExecution():
self._configure_renderer(default_denoiser="Intel")
# In case a previous renderer changed these settings
bpy.context.scene.render.image_settings.color_mode = "RGB"
bpy.context.scene.render.image_settings.file_format = "PNG"
bpy.context.scene.render.image_settings.color_depth = "8"
# check if texture less render mode is active
if self._texture_less_mode:
self.change_to_texture_less_render()
if self._use_alpha_channel:
self.add_alpha_channel_to_textures(blurry_edges=True)
self._render("rgb_")
self._register_output("rgb_", "colors", ".png", "1.0.0")
def run(self):
with Utility.UndoAfterExecution():
self._configure_renderer(default_samples=1)
if not self._avoid_rendering:
FlowRendererUtility.render(
self._determine_output_dir(), self._temp_dir,
self.config.get_bool('forward_flow', False),
self.config.get_bool('backward_flow', False),
self.config.get_bool('blender_image_coordinate_style',
False),
self.config.get_string('forward_flow_output_file_prefix',
'forward_flow_'),
self.config.get_string("forward_flow_output_key",
"forward_flow"),
self.config.get_string('backward_flow_output_file_prefix',
'backward_flow_'),
self.config.get_string("backward_flow_output_key",
"backward_flow"))
def run(self):
# if the rendering is not performed -> it is probably the debug case.
do_undo = not self._avoid_rendering
with Utility.UndoAfterExecution(perform_undo_op=do_undo):
self._configure_renderer(default_denoiser="Intel", default_samples=64)
# In case a previous renderer changed these settings
bpy.context.scene.render.image_settings.color_mode = "RGB"
bpy.context.scene.render.image_settings.file_format = "JPEG"
bpy.context.scene.render.image_settings.color_depth = "8"
bpy.context.scene.render.image_settings.quality = 98
# check if texture less render mode is active
if self._texture_less_mode:
self.change_to_texture_less_render()
if self._use_alpha_channel:
self.add_alpha_channel_to_textures(blurry_edges=True)
self._render("")
self._register_output("", "colors", ".jpg", "1.0.0")
def run(self):
# determine whether to get optical flow or scene flow - get scene flow per default
get_forward_flow = self.config.get_bool('forward_flow', False)
get_backward_flow = self.config.get_bool('backward_flow', False)
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():
self._configure_renderer(
default_samples=self.config.get_int("samples", 1))
self._output_vector_field()
# only need to render once; both fwd and bwd flow will be saved
temporary_fwd_flow_file_path = os.path.join(
self._temp_dir, 'fwd_flow_')
temporary_bwd_flow_file_path = os.path.join(
self._temp_dir, 'bwd_flow_')
self._render("bwd_flow_",
custom_file_path=temporary_bwd_flow_file_path)
# After rendering: convert to optical flow or calculate hsv visualization, if desired
if not self._avoid_rendering:
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 self.config.get_bool(
'blender_image_coordinate_style', False):
fwd_flow_field[:, :,
1] = fwd_flow_field[:, :, 1] * -1
fname = os.path.join(
self._determine_output_dir(),
self.config.get_string(
'forward_flow_output_file_prefix',
'forward_flow_')) + '%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 self.config.get_bool(
'blender_image_coordinate_style', False):
bwd_flow_field[:, :,
1] = bwd_flow_field[:, :, 1] * -1
fname = os.path.join(
self._determine_output_dir(),
self.config.get_string(
'backward_flow_output_file_prefix',
'backward_flow_')) + '%04d' % frame
np.save(fname + '.npy', bwd_flow_field[:, :, :2])
# register desired outputs
if get_forward_flow:
self._register_output(default_prefix=self.config.get_string(
'forward_flow_output_file_prefix', 'forward_flow_'),
default_key=self.config.get_string(
"forward_flow_output_key",
"forward_flow"),
suffix='.npy',
version='2.0.0')
if get_backward_flow:
self._register_output(default_prefix=self.config.get_string(
'backward_flow_output_file_prefix', 'backward_flow_'),
default_key=self.config.get_string(
"backward_flow_output_key",
"backward_flow"),
suffix='.npy',
version='2.0.0')
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 run(self):
with Utility.UndoAfterExecution():
self._configure_renderer(default_samples=1)
# Get objects with meshes (i.e. not lights or cameras)
objs_with_mats = get_all_mesh_objects()
colors, num_splits_per_dimension, used_objects = self._colorize_objects_for_instance_segmentation(
objs_with_mats)
bpy.context.scene.render.image_settings.file_format = "OPEN_EXR"
bpy.context.scene.render.image_settings.color_depth = "16"
bpy.context.view_layer.cycles.use_denoising = False
bpy.context.scene.cycles.filter_width = 0.0
if self._use_alpha_channel:
self.add_alpha_channel_to_textures(blurry_edges=False)
# Determine path for temporary and for final output
temporary_segmentation_file_path = os.path.join(self._temp_dir, "seg_")
final_segmentation_file_path = os.path.join(self._determine_output_dir(),
self.config.get_string("output_file_prefix", "segmap_"))
# Render the temporary output
self._render("seg_", custom_file_path=temporary_segmentation_file_path)
# 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
# get the type of mappings which should be performed
used_attributes = self.config.get_raw_dict("map_by", "class")
used_default_values = self.config.get_raw_dict("default_values", {})
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 = []
# After rendering
if not self._avoid_rendering:
for frame in range(bpy.context.scene.frame_start, bpy.context.scene.frame_end): # for each rendered frame
file_path = temporary_segmentation_file_path + "%04d" % frame + ".exr"
segmentation = load_image(file_path)
segmap = Utility.map_back_from_equally_spaced_equidistant_values(segmentation,
num_splits_per_dimension,
self.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
# 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 and not self._avoid_rendering:
csv_file_path = os.path.join(self._determine_output_dir(),
self.config.get_string("segcolormap_output_file_prefix",
"class_inst_col_map") + ".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)
self._register_output("segmap_", "segmap", ".npy", "2.0.0")
if save_in_csv_attributes:
self._register_output("class_inst_col_map",
"segcolormap",
".csv",
"2.0.0",
unique_for_camposes=False,
output_key_parameter_name="segcolormap_output_key",
output_file_prefix_parameter_name="segcolormap_output_file_prefix")
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)
def simulate_and_fix_final_poses(
min_simulation_time: float = 4.0,
max_simulation_time: float = 40.0,
check_object_interval: float = 2.0,
object_stopped_location_threshold: float = 0.01,
object_stopped_rotation_threshold: float = 0.1,
substeps_per_frame: int = 10,
solver_iters: int = 10):
""" Simulates the current scene and in the end fixes the final poses of all active objects.
The simulation is run for at least `min_simulation_time` seconds and at a maximum `max_simulation_time` seconds.
Every `check_object_interval` seconds, it is checked if the maximum object movement in the last second is below a given threshold.
If that is the case, the simulation is stopped.
After performing the simulation, the simulation cache is removed, the rigid body components are disabled and the pose of the active objects is set to their final pose in the simulation.
:param min_simulation_time: The minimum number of seconds to simulate.
:param max_simulation_time: The maximum number of seconds to simulate.
:param check_object_interval: The interval in seconds at which all objects should be checked if they are still moving. If all objects
have stopped moving, than the simulation will be stopped.
:param object_stopped_location_threshold: The maximum difference per second and per coordinate in the rotation Euler vector that is allowed. such
that an object is still recognized as 'stopped moving'.
:param object_stopped_rotation_threshold: The maximum difference per second and per coordinate in the rotation Euler vector that is allowed. such
that an object is still recognized as 'stopped moving'.
:param substeps_per_frame: Number of simulation steps taken per frame.
:param solver_iters: Number of constraint solver iterations made per simulation step.
"""
# Undo changes made in the simulation like origin adjustment and persisting the object's scale
with Utility.UndoAfterExecution():
# Run simulation and remember poses before and after
obj_poses_before_sim = PhysicsSimulation._get_pose()
origin_shifts = PhysicsSimulation.simulate(
min_simulation_time, max_simulation_time,
check_object_interval, object_stopped_location_threshold,
object_stopped_rotation_threshold, substeps_per_frame,
solver_iters)
obj_poses_after_sim = PhysicsSimulation._get_pose()
# Make sure to remove the simulation cache as we are only interested in the final poses
bpy.ops.ptcache.free_bake(
{"point_cache": bpy.context.scene.rigidbody_world.point_cache})
# Fix the pose of all objects to their pose at the and of the simulation (also revert origin shift)
objects_with_physics = [
MeshObject(obj) for obj in get_all_blender_mesh_objects()
if obj.rigid_body is not None
]
for obj in objects_with_physics:
# Skip objects that have parents with compound rigid body component
has_compound_parent = obj.get_parent(
) is not None and obj.get_parent().get_rigidbody(
) is not None and obj.get_parent().get_rigidbody(
).collision_shape == "COMPOUND"
if obj.get_rigidbody(
).type == "ACTIVE" and not has_compound_parent:
# compute relative object rotation before and after simulation
R_obj_before_sim = mathutils.Euler(obj_poses_before_sim[
obj.get_name()]['rotation']).to_matrix()
R_obj_after = mathutils.Euler(obj_poses_after_sim[
obj.get_name()]['rotation']).to_matrix()
R_obj_rel = R_obj_before_sim @ R_obj_after.transposed()
# Apply relative rotation to origin shift
origin_shift = R_obj_rel.transposed() @ mathutils.Vector(
origin_shifts[obj.get_name()])
# Fix pose of object to the one it had at the end of the simulation
obj.set_location(
obj_poses_after_sim[obj.get_name()]['location'] -
origin_shift)
obj.set_rotation_euler(
obj_poses_after_sim[obj.get_name()]['rotation'])
for obj in objects_with_physics:
# Disable the rigidbody element of the object
obj.disable_rigidbody()
def run(self):
with Utility.UndoAfterExecution():
self._configure_renderer(default_samples=1)
# get current method for color mapping, instance or class
method = self.config.get_string("map_by", "class")
# Get objects with materials (i.e. not lights or cameras)
objs_with_mats = [
obj for obj in bpy.context.scene.objects
if hasattr(obj.data, 'materials')
]
if method.lower() == "class":
colors, num_splits_per_dimension, color_map = self._colorize_objects_for_semantic_segmentation(
objs_with_mats)
elif method.lower() == "instance":
colors, num_splits_per_dimension, color_map = self._colorize_objects_for_instance_segmentation(
objs_with_mats)
else:
raise Exception(
"Invalid mapping method: {}, possible for map_by are: class, instance"
.format(method))
bpy.context.scene.render.image_settings.file_format = "OPEN_EXR"
bpy.context.scene.render.image_settings.color_depth = "16"
bpy.context.view_layer.cycles.use_denoising = False
bpy.context.scene.cycles.filter_width = 0.0
if self._use_alpha_channel:
self.add_alpha_channel_to_textures(blurry_edges=False)
# Determine path for temporary and for final output
temporary_segmentation_file_path = os.path.join(
self._temp_dir, "seg_")
final_segmentation_file_path = os.path.join(
self._determine_output_dir(),
self.config.get_string("output_file_prefix", "segmap_"))
# Render the temporary output
self._render("seg_",
custom_file_path=temporary_segmentation_file_path)
# 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
# After rendering
for frame in range(
bpy.context.scene.frame_start,
bpy.context.scene.frame_end): # for each rendered frame
file_path = temporary_segmentation_file_path + "%04d" % frame + ".exr"
segmentation = load_image(file_path)
segmap = Utility.map_back_from_equally_spaced_equidistant_values(
segmentation, num_splits_per_dimension,
self.render_colorspace_size_per_dimension)
segmap = segmap.astype(optimal_dtype)
fname = final_segmentation_file_path + "%04d" % frame
np.save(fname, segmap)
# write color mappings to file
if color_map is not None:
with open(os.path.join(
self._determine_output_dir(),
self.config.get_string(
"segcolormap_output_file_prefix",
"class_inst_col_map") + ".csv"),
'w',
newline='') as csvfile:
fieldnames = list(color_map[0].keys())
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
for mapping in color_map:
writer.writerow(mapping)
self._register_output("segmap_", "segmap", ".npy", "1.0.0")
if color_map is not None:
self._register_output(
"class_inst_col_map",
"segcolormap",
".csv",
"1.0.0",
unique_for_camposes=False,
output_key_parameter_name="segcolormap_output_key",
output_file_prefix_parameter_name=
"segcolormap_output_file_prefix")