def create_context(cls: type, args: argparse.Namespace,
cfg: CfgNode) -> Dict[str, Any]:
vis_specs = args.visualizations.split(",")
visualizers = []
extractors = []
for vis_spec in vis_specs:
texture_atlas = get_texture_atlas(args.texture_atlas)
texture_atlases_dict = get_texture_atlases(
args.texture_atlases_map)
vis = cls.VISUALIZERS[vis_spec](
cfg=cfg,
texture_atlas=texture_atlas,
texture_atlases_dict=texture_atlases_dict,
)
visualizers.append(vis)
extractor = create_extractor(vis)
extractors.append(extractor)
visualizer = CompoundVisualizer(visualizers)
extractor = CompoundExtractor(extractors)
context = {
"extractor": extractor,
"visualizer": visualizer,
"out_fname": args.output,
"entry_idx": 0,
}
return context
def prepare_context(vis_specs):
print('prepare context with specs: ', vis_specs)
visualizers = []
extractors = []
for vis_spec in vis_specs:
vis = VISUALIZERS[vis_spec]()
visualizers.append(vis)
extractor = create_extractor(vis)
extractors.append(extractor)
visualizer = CompoundVisualizer(visualizers)
extractor = CompoundExtractor(extractors)
context = {"extractor": extractor, "visualizer": visualizer}
return context
def create_context() -> Dict[str, Any]:
vis_specs = ["dp_contour"]
visualizers = []
extractors = []
for vis_spec in vis_specs:
vis = VISUALIZERS[vis_spec]()
visualizers.append(vis)
extractor = create_extractor(vis)
extractors.append(extractor)
visualizer = CompoundVisualizer(visualizers)
extractor = CompoundExtractor(extractors)
context = {
"extractor": extractor,
"visualizer": visualizer,
"entry_idx": 0,
}
return context
def create_context(cls: type, args: argparse.Namespace) -> Dict[str, Any]:
vis_specs = args.visualizations.split(",")
visualizers = []
extractors = []
for vis_spec in vis_specs:
vis = cls.VISUALIZERS[vis_spec]()
visualizers.append(vis)
extractor = create_extractor(vis)
extractors.append(extractor)
visualizer = CompoundVisualizer(visualizers)
extractor = CompoundExtractor(extractors)
context = {
"extractor": extractor,
"visualizer": visualizer,
"out_fname": args.output,
"entry_idx": 0,
}
return context
def __init__(
self,
model=MODELS["chimps"],
model_cache_dir: Path = Path(".zamba_cache"),
download_region=RegionEnum("us"),
):
"""Create a DensePoseManager object.
Parameters
----------
model : dict, optional (default MODELS['chimps'])
A dictionary with the densepose model defintion like those defined in MODELS.
"""
if not DENSEPOSE_AVAILABLE:
raise ImportError(
"Densepose not installed. See: https://zamba.drivendata.org/docs/stable/models/densepose/#installation"
)
# setup configuration for densepose
self.cfg = get_cfg()
add_densepose_config(self.cfg)
self.cfg.merge_from_file(model["config"])
if not (model_cache_dir / model["weights"]).exists():
model_cache_dir.mkdir(parents=True, exist_ok=True)
self.cfg.MODEL.WEIGHTS = download_weights(model["weights"],
model_cache_dir,
download_region)
# automatically use CPU if no cuda available
if not torch.cuda.is_available():
self.cfg.MODEL.DEVICE = "cpu"
self.cfg.freeze()
logging.getLogger("fvcore").setLevel(
"CRITICAL") # silence noisy detectron2 logging
# set up predictor with the configuration
self.predictor = DefaultPredictor(self.cfg)
# we have a specific texture atlas for chimps with relevant regions
# labeled that we can use instead of the default segmentation
self.visualizer = model["viz_class"](
self.cfg,
device=self.cfg.MODEL.DEVICE,
**model.get("viz_class_kwargs", {}),
)
# set up utilities for use with visualizer
self.vis_extractor = create_extractor(self.visualizer)
self.vis_embedder = build_densepose_embedder(self.cfg)
self.vis_class_to_mesh_name = get_class_to_mesh_name_mapping(self.cfg)
self.vis_mesh_vertex_embeddings = {
mesh_name: self.vis_embedder(mesh_name).to(self.cfg.MODEL.DEVICE)
for mesh_name in self.vis_class_to_mesh_name.values()
if self.vis_embedder.has_embeddings(mesh_name)
}
if "anatomy_color_mapping" in model:
self.anatomy_color_mapping = pd.read_csv(
model["anatomy_color_mapping"], index_col=0)
else:
self.anatomy_color_mapping = None
