def save_to_hdf5(output_dir_path: str,
output_data_dict: Dict[str, List[np.ndarray]],
append_to_existing_output: bool = False,
stereo_separate_keys: bool = False):
"""
Saves the information provided inside of the output_data_dict into a .hdf5 container
:param output_dir_path: The folder path in which the .hdf5 containers will be generated
:param output_data_dict: The container, which keeps the different images, which should be saved to disc.
Each key will be saved as its own key in the .hdf5 container.
:param append_to_existing_output: If this is True, the output_dir_path folder will be scanned for pre-existing
.hdf5 containers and the numbering of the newly added containers, will start
right where the last run left off.
:param stereo_separate_keys: If this is True and the rendering was done in stereo mode, than the stereo images
won't be saved in one tensor [2, img_x, img_y, channels], where the img[0] is the
left image and img[1] the right. They will be saved in separate keys: for example
for colors in colors_0 and colors_1.
"""
if not os.path.exists(output_dir_path):
os.makedirs(output_dir_path)
amount_of_frames = 0
for data_block in output_data_dict.values():
if isinstance(data_block, list):
amount_of_frames = max([amount_of_frames, len(data_block)])
# if append to existing output is turned on the existing folder is searched for the highest occurring
# index, which is then used as starting point for this run
if append_to_existing_output:
frame_offset = 0
# Look for hdf5 file with highest index
for path in os.listdir(output_dir_path):
if path.endswith(".hdf5"):
index = path[:-len(".hdf5")]
if index.isdigit():
frame_offset = max(frame_offset, int(index) + 1)
else:
frame_offset = 0
if amount_of_frames != bpy.context.scene.frame_end - bpy.context.scene.frame_start:
raise Exception(
"The amount of images stored in the output_data_dict does not correspond with the amount"
"of images specified by frame_start to frame_end.")
for frame in range(bpy.context.scene.frame_start,
bpy.context.scene.frame_end):
# for each frame a new .hdf5 file is generated
hdf5_path = os.path.join(output_dir_path,
str(frame + frame_offset) + ".hdf5")
with h5py.File(hdf5_path, "w") as file:
# Go through all the output types
print(f"Merging data for frame {frame} into {hdf5_path}")
for key, data_block in output_data_dict.items():
if frame < len(data_block):
# get the current data block for the current frame
used_data_block = data_block[frame]
if stereo_separate_keys and (
bpy.context.scene.render.use_multiview
or used_data_block.shape[0] == 2):
# stereo mode was activated
WriterUtility._write_to_hdf_file(
file, key + "_0", data_block[frame][0])
WriterUtility._write_to_hdf_file(
file, key + "_1", data_block[frame][1])
else:
WriterUtility._write_to_hdf_file(
file, key, data_block[frame])
else:
raise Exception(
f"There are more frames {frame} then there are blocks of information "
f" {len(data_block)} in the given list for key {key}."
)
blender_proc_version = Utility.get_current_version()
if blender_proc_version:
WriterUtility._write_to_hdf_file(
file, "blender_proc_version",
np.string_(blender_proc_version))
def run(self):
if self._avoid_rendering:
print("Avoid rendering is on, no output produced!")
return
if self.config.get_bool("append_to_existing_output", False):
frame_offset = 0
# Look for hdf5 file with highest index
for path in os.listdir(self._determine_output_dir(False)):
if path.endswith(".hdf5"):
index = path[:-len(".hdf5")]
if index.isdigit():
frame_offset = max(frame_offset, int(index) + 1)
else:
frame_offset = 0
# Go through all frames
for frame in range(bpy.context.scene.frame_start,
bpy.context.scene.frame_end):
# Create output hdf5 file
hdf5_path = os.path.join(self._determine_output_dir(False),
str(frame + frame_offset) + ".hdf5")
with h5py.File(hdf5_path, "w") as f:
if not GlobalStorage.is_in_storage("output"):
print("No output was designed in prior models!")
return
# Go through all the output types
print("Merging data for frame " + str(frame) + " into " +
hdf5_path)
for output_type in GlobalStorage.get("output"):
# Build path (path attribute is format string)
file_path = output_type["path"]
if '%' in file_path:
file_path = file_path % frame
# Check if file exists
if not os.path.exists(file_path):
# If not try stereo suffixes
path_l, path_r = self._get_stereo_path_pair(file_path)
if not os.path.exists(path_l) or not os.path.exists(
path_r):
raise Exception("File not found: " + file_path)
else:
use_stereo = True
else:
use_stereo = False
if use_stereo:
path_l, path_r = self._get_stereo_path_pair(file_path)
img_l, new_key, new_version = self._load_and_postprocess(
path_l, output_type["key"], output_type["version"])
img_r, new_key, new_version = self._load_and_postprocess(
path_r, output_type["key"], output_type["version"])
if self.config.get_bool("stereo_separate_keys", False):
self._write_to_hdf_file(f, new_key + "_0", img_l)
self._write_to_hdf_file(f, new_key + "_1", img_r)
else:
data = np.array([img_l, img_r])
self._write_to_hdf_file(f, new_key, data)
else:
data, new_key, new_version = self._load_and_postprocess(
file_path, output_type["key"],
output_type["version"])
self._write_to_hdf_file(f, new_key, data)
self._write_to_hdf_file(f, new_key + "_version",
np.string_([new_version]))
blender_proc_version = Utility.get_current_version()
if blender_proc_version:
self._write_to_hdf_file(f, "blender_proc_version",
np.string_(blender_proc_version))
