def run(self) -> None:
os.environ["CUDA_DEVICES_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = str(self.gpu_id)
device = torch.device("cuda:0")
processor = Preprocessor(cfg=self.opt,
proc_size=self.opt.image_size,
device=device)
while self.is_run and not self.queue.empty():
try:
meta_proc, is_ref = self.queue.get()
processed_info = ProcessInfo(meta_proc)
processed_info.deserialize()
if is_ref:
processor.execute(
processed_info,
crop_size=self.opt.image_size,
num_workers=self.opt.num_workers,
estimate_boxes_first=self.opt.Preprocess.
estimate_boxes_first,
factor=self.opt.Preprocess.Cropper.ref_crop_factor,
use_simplify=self.opt.Preprocess.use_smplify,
temporal=True,
filter_invalid=True,
inpaintor=False,
parser=False,
find_front=False,
visual=False)
else:
processor.execute(
processed_info,
crop_size=self.opt.image_size,
num_workers=self.opt.num_workers,
estimate_boxes_first=self.opt.Preprocess.
estimate_boxes_first,
factor=self.opt.Preprocess.Cropper.src_crop_factor,
use_simplify=self.opt.Preprocess.use_smplify,
temporal=False,
filter_invalid=True,
find_front=True,
parser=True,
num_candidate=self.opt.Preprocess.FrontInfo.
NUM_CANDIDATE,
render_size=self.opt.Preprocess.FrontInfo.RENDER_SIZE,
inpaintor=True,
dilate_kernel_size=self.opt.Preprocess.
BackgroundInpaintor.dilate_kernel_size,
dilate_iter_num=self.opt.Preprocess.
BackgroundInpaintor.dilate_iter_num,
bg_replace=self.opt.Preprocess.BackgroundInpaintor.
bg_replace,
visual=True,
)
except Exception("model error!") as e:
print(e.message)
def test_01_deserialize(self):
meta_input = MetaInputInfo(
path="/p300/tpami/datasets/iPER/images_HD/004/1/2", name="004/1/2")
meta_process = MetaProcess(
meta_input,
root_primitives_dir="/p300/tpami/datasets/iPER/primitives")
proc_info = ProcessInfo(meta_process)
proc_info.deserialize()
src_infos = proc_info.convert_to_src_info(num_source=8)
def digital_deform(opt) -> None:
"""
Digitalizing the source images.
Args:
opt:
Returns:
None
"""
print("\t\tPre-processing: digital deformation start...")
que = Queue()
need_to_process = 0
meta_src_proc = opt.meta_data["meta_src"]
for i, meta_proc in enumerate(meta_src_proc):
processed_info = ProcessInfo(meta_proc)
processed_info.deserialize()
if not HumanDigitalDeformConsumer.check_has_been_deformed(
processed_info):
que.put(processed_info)
need_to_process += 1
if need_to_process > 0:
MAX_PER_GPU_PROCESS = opt.Preprocess.MAX_PER_GPU_PROCESS
per_gpu_process = int(np.ceil(need_to_process / len(opt.gpu_ids)))
candidate_gpu_process = opt.gpu_ids * min(MAX_PER_GPU_PROCESS,
per_gpu_process)
num_gpu_process = min(len(candidate_gpu_process), need_to_process)
consumers = []
for gpu_process_id in range(num_gpu_process):
gpu_id = candidate_gpu_process[gpu_process_id]
consumer = HumanDigitalDeformConsumer(que, gpu_id, opt)
consumers.append(consumer)
# all processors start
for consumer in consumers:
consumer.start()
# all processors join
for consumer in consumers:
consumer.join()
print("\t\tPre-processing: digital deformation completed...")
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 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 _read_vids_info(self):
vid_infos_list = []
for meta_process in self._meta_process_list:
# print(vid_dir)
process_info = ProcessInfo(meta_process)
process_info.deserialize()
vid_info = process_info.convert_to_src_info(self._opt.num_source)
length = vid_info["length"]
if length == 0:
continue
vid_info["probs"] = self._sample_probs(vid_info)
vid_infos_list.append(vid_info)
self._num_videos += 1
self._dataset_size += vid_info["length"]
self._vids_info = vid_infos_list
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.")
def prepare_process_info(vid_name):
global MotionSynthetic_video_dir, MotionSynthetic_processed_dir
vid_path = os.path.join(MotionSynthetic_video_dir, vid_name)
name = vid_name.split(".")[0]
meta_input = MetaInputInfo(path=vid_path, name=name)
meta_process = MetaProcess(
meta_input, root_primitives_dir=MotionSynthetic_processed_dir)
process_info = ProcessInfo(meta_process)
return process_info
def merge_all_source_processed_info(opt, meta_src_proc):
# merge all source processed information
vid_info_list = []
for i, meta_src in enumerate(meta_src_proc):
"""
meta_input:
path: /p300/tpami/neuralAvatar/sources/fange_1/fange_1_ns=2
bg_path: /p300/tpami/neuralAvatar/sources/fange_1/IMG_7225.JPG
name: fange_1
primitives_dir: ../tests/debug/primitives/fange_1
processed_dir: ../tests/debug/primitives/fange_1/processed
vid_info_path: ../tests/debug/primitives/fange_1/processed/vid_info.pkl
"""
src_proc_info = ProcessInfo(meta_src)
src_proc_info.deserialize()
src_info = src_proc_info.convert_to_src_info(num_source=src_proc_info.num_sources())
vid_info_list.append(src_info)
src_info_for_inference = get_src_info_for_swapper_inference(opt, vid_info_list)
# return src_info_for_inference
return src_info_for_inference
def execute(self,
processed_info: ProcessInfo,
crop_size=512,
estimate_boxes_first=True,
factor=1.25,
num_workers=0,
use_simplify=True,
filter_invalid=True,
parser=True,
inpaintor=False,
dilate_kernel_size=19,
dilate_iter_num=3,
bg_replace=False,
find_front=True,
num_candidate=25,
render_size=256,
temporal=True,
visual=True):
"""
Args:
processed_info:
crop_size:
estimate_boxes_first:
factor:
num_workers:
use_simplify:
filter_invalid:
parser:
inpaintor:
dilate_kernel_size:
dilate_iter_num:
bg_replace:
find_front:
num_candidate:
render_size:
temporal:
visual:
Returns:
"""
meta_input = processed_info["input_info"]["meta_input"]
input_path = meta_input["path"]
bg_path = meta_input["bg_path"]
src_img_dir = process_utils.format_imgs_dir(
input_path, processed_info["src_img_dir"])
out_img_dir = processed_info["out_img_dir"]
# 1 (2). run detector to crop
if estimate_boxes_first:
if not processed_info["has_run_detector"]:
# 1.1 run detector and selector to find the active boxes
print(
f"\t1.1 Preprocessing, running {self.__class__.__name__} "
f"to detect the human boxes of {src_img_dir}...")
self._execute_detector(processed_info, factor=factor)
print(
f"\t1.1 Preprocessing, finish detect the human boxes of {src_img_dir} ..."
)
if not processed_info["has_run_cropper"]:
# 1.2 crop image
# check whether the image has been cropped or not
print(
f"\t1.2 Preprocessing, cropping all images in {src_img_dir} by estimated boxes ..."
)
self._execute_crop_img(processed_info,
crop_size,
bg_path,
num_workers=num_workers)
print(
f"\t1.2 Preprocessing, finish crop the human by boxes, and save them in {out_img_dir} ..."
)
else:
if not processed_info["has_run_cropper"]:
# 1.1 (2) crop image
print(
f"\t1.2 Preprocessing, directly resize all images in {src_img_dir} by estimated boxes...."
)
self._direct_resize_img(processed_info, crop_size, bg_path)
print(
f"\t1.2 Preprocessing, finish crop the human by boxes, and save them in {out_img_dir} ..."
)
processed_info.serialize()
# 3. run smpl estimator
if not processed_info["has_run_3dpose"]:
print(
f"\t1.3 Preprocessing, running {self.__class__.__name__} to 3D pose estimation of all images in"
f"{out_img_dir} ...")
self._execute_post_pose3d(processed_info,
use_simplify,
filter_invalid=filter_invalid,
temporal=temporal)
print(
f"\t1.3 Preprocessing, finish 3D pose estimation successfully ...."
)
processed_info.serialize()
# 4. find the front face images
if find_front and not processed_info["has_find_front"]:
print(
f"\t1.4 Preprocessing, running {self.__class__.__name__} to find {num_candidate} "
f"candidates front images in {out_img_dir} ...")
self._execute_post_find_front(processed_info, num_candidate,
render_size)
print(f"\t1.4 Preprocessing, finish find the front images ....")
processed_info.serialize()
# 5. run human parser
if parser and not processed_info["has_run_parser"]:
print(
f"\t1.5 Preprocessing, running {self.__class__.__name__} to run human matting in "
f"{processed_info['out_parse_dir']} ... ")
self._execute_post_parser(processed_info)
print(f"\t1.5 Preprocessing, finish run human matting.")
processed_info.serialize()
# 6. run background inpaintor
if inpaintor and not processed_info["has_run_inpaintor"]:
print(
f"\t1.6 Preprocessing, running {self.__class__.__name__} to run background inpainting ..."
)
self._execute_post_inpaintor(processed_info,
dilate_kernel_size=dilate_kernel_size,
dilate_iter_num=dilate_iter_num,
bg_replace=bg_replace)
print(
f"\t1.6 Preprocessing, finish run background inpainting ....")
processed_info.serialize()
if visual and not os.path.exists(processed_info["out_visual_path"]):
print(
f"\t1.7 Preprocessing, saving visualization to {processed_info['out_visual_path']} ..."
)
self._save_visual(processed_info)
print(
f"\t1.7 Preprocessing, saving visualization to {processed_info['out_visual_path']} ..."
)
processed_info["has_finished"] = True
processed_info.serialize()
print("{} has finished...".format(self.__class__.__name__))
def imitate(opt):
"""
Args:
opt:
Returns:
all_meta_outputs (list of MetaOutput):
"""
print("Step 3: running imitator.")
if opt.ip:
from iPERCore.tools.utils.visualizers.visdom_visualizer import VisdomVisualizer
visualizer = VisdomVisualizer(env=opt.model_id,
ip=opt.ip,
port=opt.port)
else:
visualizer = None
# set imitator
imitator = ModelsFactory.get_by_name("imitator", opt)
meta_src_proc = opt.meta_data["meta_src"]
meta_ref_proc = opt.meta_data["meta_ref"]
all_meta_outputs = []
for i, meta_src in enumerate(meta_src_proc):
"""
meta_input:
path: /p300/tpami/neuralAvatar/sources/fange_1/fange_1_ns=2
bg_path: /p300/tpami/neuralAvatar/sources/fange_1/IMG_7225.JPG
name: fange_1
primitives_dir: ../tests/debug/primitives/fange_1
processed_dir: ../tests/debug/primitives/fange_1/processed
vid_info_path: ../tests/debug/primitives/fange_1/processed/vid_info.pkl
"""
src_proc_info = ProcessInfo(meta_src)
src_proc_info.deserialize()
src_info = src_proc_info.convert_to_src_info(num_source=opt.num_source)
src_info_for_inference = get_src_info_for_inference(opt, src_info)
# source setup
imitator.source_setup(src_path=src_info_for_inference["paths"],
src_smpl=src_info_for_inference["smpls"],
masks=src_info_for_inference["masks"],
bg_img=src_info_for_inference["bg"],
offsets=src_info_for_inference["offsets"],
links_ids=src_info_for_inference["links"],
visualizer=visualizer)
for j, meta_ref in enumerate(meta_ref_proc):
"""
meta_input:
path: /p300/tpami/neuralAvatar/references/videos/bantangzhuyi_1.mp4
bg_path:
name: bantangzhuyi_1
audio: /p300/tpami/neuralAvatar/references/videos/bantangzhuyi_1.mp3
fps: 30.02
pose_fc: 400.0
cam_fc: 150.0
primitives_dir: ../tests/debug/primitives/bantangzhuyi_1
processed_dir: ../tests/debug/primitives/bantangzhuyi_1/processed
vid_info_path: ../tests/debug/primitives/bantangzhuyi_1/processed/vid_info.pkl
"""
meta_output = MetaImitateOutput(meta_src, meta_ref)
ref_proc_info = ProcessInfo(meta_ref)
ref_proc_info.deserialize()
ref_info = ref_proc_info.convert_to_ref_info()
results_dict = call_imitator_inference(
opt,
imitator,
meta_output,
ref_paths=ref_info["images"],
ref_smpls=ref_info["smpls"],
visualizer=visualizer)
# save to video
fuse_src_ref_multi_outputs(meta_output.out_mp4,
src_info_for_inference["paths"],
results_dict["ref_imgs_paths"],
results_dict["outputs"],
audio_path=meta_output.audio,
fps=meta_output.fps,
image_size=opt.image_size,
pool_size=opt.num_workers)
all_meta_outputs.append(meta_output)
for meta_output in all_meta_outputs:
print(meta_output)
print("Step 3: running imitator done.")
return all_meta_outputs
def swap(opt):
"""
Args:
opt:
Returns:
all_meta_outputs (list of MetaOutput):
"""
print("Step 3: running swapper.")
if opt.ip:
from iPERCore.tools.utils.visualizers.visdom_visualizer import VisdomVisualizer
visualizer = VisdomVisualizer(env=opt.model_id, ip=opt.ip, port=opt.port)
else:
visualizer = None
# set imitator
swapper = ModelsFactory.get_by_name("swapper", opt)
# merge all sources
meta_src_proc = opt.meta_data["meta_src"]
src_info_for_inference = merge_all_source_processed_info(opt, meta_src_proc)
# update number source
opt.num_source = sum(src_info_for_inference["num_source"])
print(f"update the number of sources {src_info_for_inference['num_source']} = {opt.num_source}")
# source setup
swapper.swap_source_setup(
src_path_list=src_info_for_inference["paths"],
src_smpl_list=src_info_for_inference["smpls"],
masks_list=src_info_for_inference["masks"],
bg_img_list=src_info_for_inference["bg"],
offsets_list=src_info_for_inference["offsets"],
links_ids_list=src_info_for_inference["links"],
swap_parts=src_info_for_inference["swap_parts"],
visualizer=visualizer,
swap_masks=None
)
# call swap
all_meta_outputs = []
# check whether it has reference or not
meta_ref_proc = opt.meta_data["meta_ref"]
for j, meta_ref in enumerate(meta_ref_proc):
"""
meta_input:
path: /p300/tpami/neuralAvatar/references/videos/bantangzhuyi_1.mp4
bg_path:
name: bantangzhuyi_1
audio: /p300/tpami/neuralAvatar/references/videos/bantangzhuyi_1.mp3
fps: 30.02
pose_fc: 400.0
cam_fc: 150.0
primitives_dir: ../tests/debug/primitives/bantangzhuyi_1
processed_dir: ../tests/debug/primitives/bantangzhuyi_1/processed
vid_info_path: ../tests/debug/primitives/bantangzhuyi_1/processed/vid_info.pkl
"""
meta_output = MetaSwapImitateOutput(meta_src_proc, meta_ref)
ref_proc_info = ProcessInfo(meta_ref)
ref_proc_info.deserialize()
ref_info = ref_proc_info.convert_to_ref_info()
results_dict = call_imitator_inference(
opt, swapper, meta_output,
ref_paths=ref_info["images"],
ref_smpls=ref_info["smpls"],
visualizer=visualizer
)
# save to video
fuse_src_ref_multi_outputs(
meta_output.out_mp4, src_info_for_inference["src_paths"],
results_dict["ref_imgs_paths"], results_dict["outputs"],
audio_path=meta_output.audio, fps=meta_output.fps,
image_size=opt.image_size, pool_size=opt.num_workers
)
all_meta_outputs.append(meta_output)
for meta_output in all_meta_outputs:
print(meta_output)
print("Step 3: running swapper done.")
return all_meta_outputs
def novel_view(opt):
"""
Args:
opt:
Returns:
all_meta_outputs (list of MetaOutput):
"""
print("Step 3: running novel viewer.")
if opt.ip:
from iPERCore.tools.utils.visualizers.visdom_visualizer import VisdomVisualizer
visualizer = VisdomVisualizer(env=opt.model_id,
ip=opt.ip,
port=opt.port)
else:
visualizer = None
# set imitator
viewer = ModelsFactory.get_by_name("viewer", opt)
meta_src_proc = opt.meta_data["meta_src"]
all_meta_outputs = []
for i, meta_src in enumerate(meta_src_proc):
"""
meta_input:
path: /p300/tpami/neuralAvatar/sources/fange_1/fange_1_ns=2
bg_path: /p300/tpami/neuralAvatar/sources/fange_1/IMG_7225.JPG
name: fange_1
primitives_dir: ../tests/debug/primitives/fange_1
processed_dir: ../tests/debug/primitives/fange_1/processed
vid_info_path: ../tests/debug/primitives/fange_1/processed/vid_info.pkl
"""
src_proc_info = ProcessInfo(meta_src)
src_proc_info.deserialize()
src_info = src_proc_info.convert_to_src_info(num_source=opt.num_source)
src_info_for_inference = get_src_info_for_inference(opt, src_info)
# source setup
viewer.source_setup(src_path=src_info_for_inference["paths"],
src_smpl=src_info_for_inference["smpls"],
masks=src_info_for_inference["masks"],
bg_img=src_info_for_inference["bg"],
offsets=src_info_for_inference["offsets"],
links_ids=src_info_for_inference["links"],
visualizer=visualizer)
novel_smpls = create_T_pose_novel_view_smpl(length=180)
novel_smpls[:, -10:] = src_info_for_inference["smpls"][0, -10:]
if not opt.T_pose:
novel_smpls[:, 6:-10] = src_info_for_inference["smpls"][0, 6:-10]
novel_smpls = add_hands_params_to_smpl(novel_smpls,
viewer.body_rec.np_hands_mean)
meta_output = MetaNovelViewOutput(meta_src)
out_imgs_dir = clear_dir(meta_output.out_img_dir)
outputs = viewer.inference(tgt_smpls=novel_smpls,
cam_strategy="smooth",
output_dir=out_imgs_dir,
visualizer=visualizer,
verbose=True)
fuse_source_output(meta_output.out_mp4,
src_info_for_inference["paths"],
outputs,
audio_path=None,
fps=25,
image_size=opt.image_size,
pool_size=opt.num_workers)
all_meta_outputs.append(meta_output)
for meta_output in all_meta_outputs:
print(meta_output)
print("Step 3: running novel viewer done.")
return all_meta_outputs
def imitate(opt):
"""
Args:
opt:
Returns:
all_meta_outputs (list of MetaOutput):
"""
print("Step 3: running imitator.")
if opt.ip:
from iPERCore.tools.utils.visualizers.visdom_visualizer import VisdomVisualizer
visualizer = VisdomVisualizer(env=opt.model_id,
ip=opt.ip,
port=opt.port)
else:
visualizer = None
# set imitator
imitator = ModelsFactory.get_by_name("imitator", opt)
meta_src_proc = opt.meta_data["meta_src"]
meta_ref_proc = opt.meta_data["meta_ref"]
all_meta_outputs = []
for i, meta_src in enumerate(meta_src_proc):
"""
meta_input:
path: /p300/tpami/neuralAvatar/sources/fange_1/fange_1_ns=2
bg_path: /p300/tpami/neuralAvatar/sources/fange_1/IMG_7225.JPG
name: fange_1
primitives_dir: ../tests/debug/primitives/fange_1
processed_dir: ../tests/debug/primitives/fange_1/processed
vid_info_path: ../tests/debug/primitives/fange_1/processed/vid_info.pkl
"""
src_proc_info = ProcessInfo(meta_src)
src_proc_info.deserialize()
src_info = src_proc_info.convert_to_src_info(num_source=opt.num_source)
src_info_for_inference = get_src_info_for_inference(opt, src_info)
# 1. personalization
imitator.source_setup(src_path=src_info_for_inference["paths"],
src_smpl=src_info_for_inference["smpls"],
masks=src_info_for_inference["masks"],
bg_img=src_info_for_inference["bg"],
offsets=src_info_for_inference["offsets"],
links_ids=src_info_for_inference["links"],
visualizer=visualizer)
for j, meta_ref in enumerate(meta_ref_proc):
"""
meta_input:
path: /p300/tpami/neuralAvatar/references/videos/bantangzhuyi_1.mp4
bg_path:
name: bantangzhuyi_1
audio: /p300/tpami/neuralAvatar/references/videos/bantangzhuyi_1.mp3
fps: 30.02
pose_fc: 400.0
cam_fc: 150.0
primitives_dir: ../tests/debug/primitives/bantangzhuyi_1
processed_dir: ../tests/debug/primitives/bantangzhuyi_1/processed
vid_info_path: ../tests/debug/primitives/bantangzhuyi_1/processed/vid_info.pkl
"""
ref_proc_info = ProcessInfo(meta_ref)
ref_proc_info.deserialize()
ref_info = ref_proc_info.convert_to_ref_info()
ref_imgs_paths = ref_info["images"]
ref_smpls = ref_info["smpls"]
ref_smpls = add_hands_params_to_smpl(
ref_smpls, imitator.body_rec.np_hands_mean)
meta_output = MetaOutput(meta_src, meta_ref)
# if there are more than 10 frames, then we will use temporal smooth of smpl.
if len(ref_smpls) > 10:
ref_smpls = temporal_smooth_smpls(ref_smpls,
pose_fc=meta_output.pose_fc,
cam_fc=meta_output.cam_fc)
out_imgs_dir = clear_dir(meta_output.imitation_dir)
outputs = imitator.inference(tgt_paths=ref_imgs_paths,
tgt_smpls=ref_smpls,
cam_strategy=opt.cam_strategy,
output_dir=out_imgs_dir,
visualizer=visualizer,
verbose=True)
fuse_source_reference_output(
meta_output.imitation_mp4,
src_info_for_inference["paths"],
ref_imgs_paths,
outputs,
# sorted(glob.glob(os.path.join(meta_output.imitation_dir, "pred_*"))),
audio_path=meta_output.audio,
fps=meta_output.fps,
image_size=opt.image_size,
pool_size=opt.num_workers)
all_meta_outputs.append(meta_output)
for meta_output in all_meta_outputs:
print(meta_output)
print("Step 3: running imitator done.")
return all_meta_outputs