def post_update_opt(opt):
"""
Post update the configurations based on the results of preprocessing.
Args:
opt:
Returns:
"""
meta_src_proc = opt.meta_data["meta_src"]
valid_meta_src_proc = []
cur_num_source = 1
for meta_proc in meta_src_proc:
process_info = ProcessInfo(meta_proc)
process_info.deserialize()
# check it has been processed successfully
if process_info.check_has_been_processed(process_info.vid_infos,
verbose=False):
valid_meta_src_proc.append(meta_proc)
num_source = process_info.num_sources()
cur_num_source = max(cur_num_source, num_source)
else:
# otherwise, clean this inputs
process_info.declare()
meta_ref_proc = opt.meta_data["meta_ref"]
valid_meta_ref_proc = []
for meta_proc in meta_ref_proc:
if meta_proc.check_has_been_processed(verbose=False):
valid_meta_ref_proc.append(meta_proc)
## 3.1 update the personalization.txt
checkpoints_dir = opt.meta_data["checkpoints_dir"]
with open(os.path.join(checkpoints_dir, "personalization.txt"),
"w") as writer:
for meta_src in valid_meta_src_proc:
writer.write(meta_src.primitives_dir + "\n")
# update the number sources
print(f"the current number of sources are {cur_num_source}, "
f"while the pre-defined number of sources are {opt.num_source}. ")
opt.num_source = min(cur_num_source, opt.num_source)
# update the source information
opt.meta_data["meta_src"] = valid_meta_src_proc
# update the reference information
opt.meta_data["meta_ref"] = valid_meta_ref_proc
return opt
def process_data():
global args, iPER_train_txt, iPER_val_txt
# 1. dumps videos to frames
# 1. prepreocess
vid_names = get_video_dirs(iPER_train_txt) + get_video_dirs(iPER_val_txt)
src_paths = prepare_src_path(vid_names)
args.src_path = "|".join(src_paths)
# print(args.src_path)
# set this as empty when preprocessing the training dataset.
args.ref_path = ""
cfg = setup(args)
# 1. human estimation, including 2D pose, tracking, 3D pose, parsing, and front estimation.
human_estimate(opt=cfg)
# 2. digital deformation.
digital_deform(opt=cfg)
# 3. check
meta_src_proc = cfg.meta_data["meta_src"]
invalid_meta_process = []
print(f"Check all videos have been processed...")
for meta_proc in tqdm(meta_src_proc):
process_info = ProcessInfo(meta_proc)
process_info.deserialize()
# check it has been processed successfully
if not process_info.check_has_been_processed(process_info.vid_infos,
verbose=False):
invalid_meta_process.append(meta_proc)
num_invalid = len(invalid_meta_process)
if num_invalid > 0:
for meta_proc in invalid_meta_process:
print(f"invalid meta proc {meta_proc}")
else:
print(f"process successfully.")
def process_data():
# 1. preprocess
src_paths = prepare_src_path()
args.src_path = "|".join(src_paths)
print(args.src_path)
# set this as empty when preprocessing the training dataset.
args.ref_path = ""
cfg = setup(args)
# 1. human estimation, including 2D pose, tracking, 3D pose, parsing, and front estimation.
human_estimate(opt=cfg)
# 2. digital deformation.
digital_deform(opt=cfg)
# 3. check
meta_src_proc = cfg.meta_data["meta_src"]
invalid_meta_process = []
for meta_proc in meta_src_proc:
process_info = ProcessInfo(meta_proc)
process_info.deserialize()
# check it has been processed successfully
if not process_info.check_has_been_processed(process_info.vid_infos,
verbose=False):
invalid_meta_process.append(meta_proc)
num_invalid = len(invalid_meta_process)
if num_invalid > 0:
for meta_proc in invalid_meta_process:
print(f"invalid meta proc {meta_proc}")
else:
print(f"process successfully.")